In response to the NO_x emission peak phenomenon in medium and high load conditions under the propulsion characteristics in diesel engines, as well as the urgent need to reduce fuel consumption due to rising fuel prices, this study adjusts multiple control parameters of a diesel/methanol compound combustion （DMCC） engine to achieve simultaneous reductions in NO_x emission and the brake specific fuel consumption （BSFC） under the premise of ensuring the power performance. To avoid the increased cost caused by large-scale experiments，predictive models for NO_x volume fraction，BSFC，and indicated power of the DMCC engine are established based on Gaussian process regression. These models are then combined with the non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） to optimize NO_x volume fraction and BSFC. The Pareto front solutions obtained are further analyzed using the technique for order preference by similarity to an ideal solution （TOPSIS） to find the optimal control parameter combination. Finally，the optimal control parameters are calibrated into the electronic control unit and compared with the original engine data. The results show that the predictive models based on Gaussian process regression achieve a goodness of fit greater than 0.95 and a root mean square error of less than 1，indicating good consistency and accuracy. Compared to the parameters before optimization （the original engine conditions），using the optimal control parameters obtained by NSGA-Ⅱ results in a 74.5% reduction in NO_x emissions to just 3.47 g/（kW•h），and an average BSFC reduction of 6.7% to 203.5 g/（kW•h）.