Abstract: Abstract: China Stage III emission standard for diesel engine of non-road mobile machinery has been executed since April 1, 2016. Some manufactures have begun to adopt a high pressure common rail system to cope with this emission regulation. Fuel injection parameters can be adjusted flexibly by using the high pressure common rail system. Therefore, combustion process can be improved. However, it brings the problem of increasing the workload of the calibration and optimization significantly. With the increasing of calibration variables, the calibration combinations will increase exponentially. The traditional calibration method, such as the single variable search method or the single variable sweep method, their calibration results may not be able to make the engine to achieve best performance, especially when the number of calibration parameters is more than two. Nowadays, a majority of optimization calibration methods are using a non-parametric modeling method to fit the calibration model and optimizing the calibration model by using an intelligent optimization algorithm. However, the non-parametric modeling method cannot give the descriptions of the model structure or the model coefficients. Meanwhile, it needs a large number of test data to fit an accurate calibration model. Moreover, the non-parametric modeling method and the intelligent optimization algorithm are both very complex. A polynomial modeling method has a good compromise between complexity and computational efficiency of the model. Therefore, in allusion to the calibration stage of a non-road high pressure common rail diesel engine, 4 calibration variables, i.e., main injection quantity, pilot injection quantity, main injection timing and injection pressure, were chosen as the factors. The non-road diesel engine design index and related constraint parameters were chosen as the responses at the peak torque speed of 1 600 r/min and the rated power speed of 2 600 r/min, respectively. The reasonable factor levels of the design of experiments (DoE) were selected. By using the response surface methodology (RSM) of Box-Behnken design, the DoE matrices were obtained at the engine speed of 1 600 r/min and 2 600 r/min, respectively. Meanwhile, the corresponding test was conducted according to the experiment design. The second order regression models of all the responses were got and evaluated. The interaction effects of the 4 calibration parameters on engine performance were investigated by using the RSM. The corresponding optimization was conducted respectively at the engine speed of 1 600 and 2 600 r/min taking the target torque and target power as the setting target under the principle of the minimum brake specific fuel consumption, the maximum air-to-fuel ratio, the minimum peak cylinder pressure and gas temperature of exhaust manifold. The combination of calibration variables was obtained at 2 engine speeds, and the proposed method was verified by experiments. The results showed that all the quadratic response surface regression models had a good accuracy and a good predictive ability. The determination coefficient R2, the adjusted determination coefficient R2 adj, and the prediction determination coefficient R2 pred were all above 0.92. The maximum error between test value and predicted value was less than 3.07 %. With the optimized calibration parameters, the peak torque and the rated power of the non-road high pressure common rail diesel engine reached 200.7 N(m and 40.1 kW, respectively. The engine achieved the design index, and moreover, the brake specific fuel consumption, air-to-fuel ratio, peak cylinder pressure, and gas temperature of exhaust manifold were all under the range of acceptance. It is feasible to optimize the diesel engine design point by using the RSM.