For the automated warehousing scheduling problem with multi-shuttles, an improved bacterial foraging algorithm is proposed. Firstly, the chemotactic stepsize is adaptively adjusted to guide search toward the optimum solution. Then, the heuristic elimination and dispersal strategy is devised based on the level of individual contributing rate to the diversity of population, which reduces the probability of falling into local optimum. The strategy of preserving some of infeasible solutions is adopted to increase the chances of obtaining the optimal solution. Finally, the convergence of the proposed algorithm is proved and the performance is tested through simulation combined with the industrial real world case. Results show that the proposed algorithm achieves better performance in terms of the solution quality and the convergence efficiency for the automated warehousing scheduling problem with multi-shuttles.