Abstract: For the multi-objective flexible job-shop scheduling problem, an enhanced sorting dung beetle optimizer/strengthened (NSDBO/S) algorithm is designed. Firstly, a scheduling model is established to minimize the maximum processing time, the maximum machine load and the total machine load. Secondly, the hybrid improved sine cosine Cauchy mutation operator and Levy flight strategy are introduced to enhance the global optimization ability of the non dominated dung beetle algorithm. For flexible job shop scheduling problem, chaotic mapping is used to improve the quality of initial solution, and the machine pre learning mechanism is used to improve the convergence speed and accuracy of NSDBO/S for flexible job shop scheduling problem. Considering that the multi-objective flexible job shop scheduling problem has a large solution space and is easy to fall into local optimization, a neighborhood structure is designed for the Pareto solution set generated by each iteration to improve the local optimization ability of the algorithm. Finally, in order to verify the feasibility of NSDBO/S in solving the multi-objective flexible job shop scheduling problem, simulation experiments are carried out on classic examples and precision shaft production examples. The experimental results show that NSDBO/S is superior to other comparative algorithms in the quality of the solution of classical examples and practical problems, especially in large-scale problems. The nonparametric test of the experimental data further verifies the statistical significance of NSDBO/S.