In this paper, a five-part study on the molding quality of box-type thin-walled injection molded parts with volume shrinkage and warpage deformation was conducted. In the first part, the molding parameters: filling time, plastic temperature, mold temperature (C), holding time (D), maximum holding pressure (E), and cooling time (F) were selected, and a six-factor, five-level L25 (5 6 ) orthogonal experimental design (OED) was designed. Then, the optimal combination of molding parameters was optimized by using gray correlation (GC) theory analysis. In the second part, the six molding parameters from the first part were further optimized based on the Box Behnken Design (BBD) response surface method (RSM) and BP neural network (BPNN) training, and the results showed that the fitted regression model after BPNN training had a smaller prediction error. In the third part, the regression models fitted with BBD and BPNN-BBD are further optimized globally. The optimization search method uses the NSGA-II genetic optimization algorithm. And comparing the above four optimization methods found that BPNN-BBD-NSGA-II prediction error < BPNN-BBD prediction error < BBD-NSGA-II prediction error < BBD prediction error. The fourth part is based on the anti-deformation design (ADD) theory. The NX/Moldex3D global optimization platform is created to finally almost completely eliminate warpage deformation of box-type thin-walled injection molded parts. In the last part, a trial mold verification analysis was performed, and the molding quality data was obtained by using optical scanning instruments, and the GOM software was used to compare and analyze the deviation values of each appearance surface and the flatness values.