With the development of aeronautical manufacturing technology and the wide application of flexible production lines, reasonable multi-objective production scheduling method has become a key factor to balance enterprise interests and green development. Therefore, the multi-objective production scheduling optimization problem (FJSP) oriented to flexible production lines has become increasingly prominent. In order to solve this problem, this paper proposes an improved high-dimensional multi-objective particle swarm optimization algorithm (PMO-PSO) based on Pareto domination. Firstly, considering the order completion time, carbon emission and load balancing of production equipment, a mathematical model of multi-objective production scheduling optimization was established. Secondly, the particle swarm optimization algorithm was introduced to improve the local search ability of particles and increase the possibility of jumping out of the local optimal by means of particle crossing, mutation and simulated annealing operation. Then, a global optimal particle decision mechanism based on fuzzy optimization method is designed to select the optimal frontier of high-dimensional multi-objective Pareto. Finally, through the simulation test of flexible production scheduling, it is verified that the proposed PMO-PSO algorithm can obtain better Pareto optimal frontier distribution characteristics.