The single mechanism based Grey Wolf Optimizer (GWO) is easy to fall into local optimum and has slow convergence speed. In order to solve the problems, an Improved Grey Wolf Optimization (IGWO) was proposed to solve the actual location selection problem of railway logistics distribution center. Firstly, based on the basic GWO, the theory of good point set was introduced to initialize the population, which improved the diversity of the initial population. Then, the D-value Elimination Strategy (DES) was used to increase the global optimization ability, so as to achieve an efficient optimization mode. The simulation results show that, compared with the standard GWO, IGWO has the fitness value increased by 3%, and the accuracy of the optimal value increased by up to 7 units in 10 test functions. Compared with Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Genetic Algorithm (GA), IGWO has the location selection speed increased by 39.6%, 46.5% and 65.9% respectively, and the location selection velocity is significantly improved. The proposed algorithm can be used for railway logistics center location selection.