In order to improve the operation efficiency of the three-dimensional warehouse, aiming at stacker path scheduling problem, a stacking machine scheduling optimization model is established based on the time, energy consumption, and operation efficiency, and an Improved Multi-Objective Genetic Algorithm (IMOGA) is proposed. In IMOGA, genetic operator is improved based on NSGA-Ⅱ, crossover and mutation operations are designed for this model, adaptive genetic operator is introduced, and a local random search strategy based on the simulated annealing is added. The IMOGA is validated through the stacker scheduling situation in a spandex factory warehouse. The results show that convergence speed of IMOGA is faster, the quality of the solution set is higher, and it has higher applicability in stacker scheduling.