Aiming at the problems of biogeographybased optimization algorithm (BBO) in solving complex job shop scheduling problems (JSP), an improved differential evolution biogeographybased optimization algorithm is proposed. By effectively combining the searchability of differential evolution algorithm (DE) with the utilization of biogeographybased optimization algorithm, at the same time, elite retention mechanism is adopted to retain individuals with higher fitness, and inertial weight strategy is introduced to adjust the proportion of mutation operation in hybrid migration operation to improve the global search ability of the algorithm, then increase the disturbance in the small probability in order to prevent the algorithm as the iteration progressed into a local optimal solution. Finally, different test functions and job shop scheduling problems are used for experiments. The results show that the improved algorithm has better performance in convergence speed and optimization results.