The multi-objective flexible job shop scheduling problem in intelligent manufacturing actual production scenarios considering transportation time and machine maintenance activities is studied. An improved multi-layer genetic algorithm is proposed, which integrates equipment constraints into the comprehensive scheduling model while considering energy consumption, minimizing the maximum completion time, key machine load, and total machine load to optimize the production cycle. The experimental results with the particle swarm optimization algorithm demonstrate that the scheduling algorithm exhibits good stability under different scales of test cases, proving its effectiveness in practical applications. Continuous improvement is expected to yield more effective manufacturing solutions to adapt to changing demands.