Taking the scheduling problem in the flexible manufacturing of a large furniture enterprise as the research object, this study proposes a multi-strategy whale optimization algorithm (MWOA), which is mainly used to solve the flexible job shop scheduling problem. First, in order to improve the diversity of the initial population, chaos theory is introduced to initialize the population; at the same time, the nonlinear convergence factor and adaptive inertia weight coefficient are designed to balance the global exploration and local development capabilities; then the differential evolution (DE) operator is used to improve the utilization and search ability of WOA. Finally, the optimal individual chaotic search strategy is adopted to reduce the probability of premature convergence of WOA. With the objective of minimizing the maximum completion time, the benchmark test problem and the scheduling optimization problem of the manufacturing process of a furniture enterprise are solved. The results show that MWOA overcomes the shortcomings of the basic WOA, such as low optimization accuracy, slow convergence speed, and easy falling into local optimization. Compared with the comparison algorithm, MWOA achieves better optimization results.