In response to the characteristics of large workload and complex categories in automated three-dimensional warehouses, the optimization research was conducted on warehouse storage spaces. Following the principles of turnover rate, shelf stability, and correlation, a storage space optimization model was constructed to improve outbound efficiency, shelf stability, and cargo correlation. A comparative analysis was conducted using traditional simulated annealing algorithm, genetic algorithm, and improved simulated annealing genetic algorithm to solve the model’s cargo location optimization results. It was proved that the improved algorithm is more effective than the traditional basic algorithm in solving the cargo location optimization problem, which helps to improve the current situation of low space utilization, low picking efficiency, and chaotic cargo placement in automated three-dimensional warehouses.