The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial product design and meet the personalized customization needs of customers. Starting from computer-aided technology, this paper illustrates the optimization of CAD design by using a genetic algorithm and constructs a bipolar progressive interactive genetic algorithm using bipolar progressive ranking and fuzzy fitness jointly. Genetic algorithm genotypes in CAD process product design are defined, and a comparative evaluation experimental analysis of industrial product design is performed with the AR-IGA algorithm. Regarding the number of iterations, the average number of iterations for the AR-IGA algorithm to complete the color-matching design of process products among ten testers was 9.34, which was 36.42% lower than that of the TIGA algorithm. The average evaluation elapsed time of the AR-IGA algorithm was 246.9s, which was 24.12% less than the average evaluation elapsed time of the TIGA algorithm. This shows that computer-aided technology optimized by genetic algorithms can effectively improve the efficiency of industrial product design and satisfy more customers to realize personalized design simultaneously.