Abstract: To solve the issue of configuring AGV quantity for a material handling system in a flexible job shop, an optimization model with dual constraints of system output rate and production cycle is established to minimize the AGV purchase cost. Since the optimization problem is a stochastic nonlinear integer programming one, and the constraints cannot be expressed in a closed form of decision variables, a simulation based on particle swarm optimization algorithm is proposed to solve it. For a flexible job shop with random batch transportation, a performance estimation model is established based on a discrete event simulation platform, and a particle swarm optimization algorithm embedded in the simulation model is proposed to generate the optimization scheme of AGV quantity configuration. Through simulation experiments and comparison of different optimization methods, it shows that the proposed method has an average improvement of 8% and 8.9% in terms of superiority and stability compared with other algorithms. The optimized configuration scheme is determined by analyzing a practical application case, and the results verify the effectiveness of the proposed method, which has practical application value.