This paper presents a multi-objective optimization approach combined with discrete event simulation to improve the efficiency of aerospace electronic component assembly lines. By integrating genetic algorithms with simulation modeling, the study targets key objectives: minimizing cycle time, reducing production costs, and maximizing resource utilization. Results from real-world implementation revealed a 6.66 % increase in production for Product A, 8.54% for Product B, a 10% reduction in average cycle time, and a 12.4 % decrease in work-in-process (WIP). Additionally, station utilization improved by up to 10 %, and average waiting cost per unit dropped by 11.9 %. This framework not only enhanced operational performance but also provided a robust decision-support tool for high-reliability manufacturing environments in the aerospace industry.