<p>In the face of increasingly fierce global competition, improving manufacturing efficiency and innovation ability is the key for manufacturing enterprises to survive and develop. The purpose of this paper is to propose an optimization scheme for intelligent manufacturing system based on improved genetic algorithm, promote its collaboration with industrial design, and achieve efficient and low-cost product development. In view of the traditional genetic algorithm, the elite retention strategy is introduced to make the best individuals in each generation directly enter the next generation to avoid the loss of high-quality genes, and at the same time, the crossover probability and mutation rate are dynamically adjusted, and a higher crossover probability is set in the early stage of the algorithm to increase population diversity and quickly search for solution space, and the crossover probability and variation probability are reduced and the mutation probability is increased in the later stage, so as to avoid falling into the local optimal and finely search for the global optimal solution. Experiments in many fields such as electronics, machinery, and consumer goods show that the improved genetic algorithm can improve the search efficiency of the solution by about 25%, reduce the calculation time by nearly half on average, and have more prominent effects in the face of high-dimensional multi-constraint design problems, such as in complex auto parts design projects, the performance parameters are accurately matched with the cost control target, the design cycle is shortened by 40%, and the innovation index is increased by more than 20%, which strongly proves the important role of collaborative optimization in improving design quality and production efficiency. It can be seen that the integration of improved genetic algorithm into intelligent manufacturing system and industrial design process can significantly enhance the competitiveness of enterprises, promote the development of the industry in the direction of intelligence and personalization, and is an important driving force for the future development of the industry.</p> Graphical Abstract <p></p>