Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods.