In order to improve the stamping quality of automobile engine hood inner panel and reduce the maximum thinning rate and the maximum thickening rate of stamping parts, a stamping parameter optimization method based on neural network-strong propagation NSGA-II algorithm was proposed, and a multi-objective optimization model for reducing the maximum thinning rate and the maximum thickening rate was established. Then, the sampling points in the thinking space were extracted by using the optimal Latin sampling method, and the performance parameters of the sampling points were obtained according to the numerical simulation. Furthermore, through using BP neural network to fit the relationship between stamping parameters and quality parameters, it was verified that the regression accuracy was high, and the BP neural network could be used to predict quality parameters. Finally, the multi-point random crossover and row crossover random mutation methods were defined and applied to NSGA-II algorithm, and the solution method of optimized model based on strong reproduction NSGA-II algorithm was given. The verification results show that the Pareto solution set of strong reproduction NSGA-II algorithm can dominate the solution set of NSGA-II algorithm, which verifies the effectiveness of the improved strategy. After optimization, the average values of the maximum thinning rate and the maximum thickening rate are reduced by 15.14% and 18.93% respectively, which verifies the effectiveness and superiority of the optimized method.