In fields such as manufacturing, logistics, and communication networks, equipment layout optimization is a significant and prevalent issue. This paper investigates an equipment layout optimization method in complex environments based on a genetic algorithm (CE-GA). Initially, we formulate the layout optimization problem mathematically, defining the optimization objectives and constraints. Subsequently, we propose and improve the GA, incorporating initial population generation, fitness function design, selection, crossover, mutation operations, and elite retention strategy. Experiments validate the superiority of this method in terms of convergence speed and solution quality, comparing it with simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The experimental results demonstrate that the improved GA performs excellently in handling layout optimization problems in complex environments. Finally, this paper summarizes the research findings, highlights contributions and limitations, and suggests future research directions. This study has significant practical implications for improving system efficiency and reducing operational costs, providing a valuable reference for future layout optimization research.