The main objective of the present article is to obtain the optimum technological parameters of the automobile plastic front-end frame. Moldflow software was used to simulate the injection molding of the automobile plastic front-end frame bracket in this study. The uniformity experiment was designed and completed. Five injection molding process parameters, including mold temperature, melt temperature, packing pressure, injection time, and packing time, were selected as experimental factors. Volume shrinkage and warpage amount were selected as quality evaluation indexes. Statistical Product Service Solutions (SPSS) software was used to perform linear regression analysis on the test results, respectively, and the linear regression equation corresponding to the two evaluation indexes was obtained. Then, regression equation analysis was carried out to obtain the optimal process parameter combination of the volume shrinkage and the warpage amount. A back propagation (BP) neural network model with input as a process parameter and output as an evaluation index is established by MATLAB and optimized by a genetic algorithm (GA). Finally, the optimized neural network model was used to predict the combination of process parameters with the minimum volume shrinkage and warpage amount. Based on the performed simulations, the minimum volume shrinkage and the minimum amount of warping in the optimal design were 13% and 0.7062 mm, respectively. The corresponding combination of process parameters was the mold temperature of 75.5°C, the melt temperature of 285.32°C, the packing pressure of 42.32 MPa, the injection time of 3.45 s, and the packing time of 60 s. According to the optimal process parameters, the volume shrinkage and warpage amount of the automobile plastic front-end frame have reached the optimal state at the same time, which improves the quality of injection molding and reduces production and processing costs. It has a certain guiding significance.