For Multi-factor Flexible Job shop Green Scheduling Problem with Setup and Transportation time constraints and Variable machine processing Speed （MFJGSP-STVS）， a mathematical model with completion time and energy consumption as optimization objectives was constructed， and an Enhanced Multi-objective Evolutionary Algorithm （EMoEA） was proposed to solve the problem. In the algorithm， a three-layer integer encoding method was adopted， Machine Idle time Preference （MIP） rule and Turning On/Off strategy （TOF） were applied in the decoding to optimize the objectives， and heuristic rules such as Global Search （GS） were employed to generate the initial population； a cluster crossover approach was designed on the basis of non-dominated hierarchy idea， so as to accelerate the algorithm’s convergence； to prevent the algorithm from converging prematurely and falling into the local optimum， a derivation strategy was proposed to diffuse the non-dominated solution set， and an adaptive local search strategy based on critical path was designed to further enhance the exploration capability of the algorithm in solution space. Simulation results show that each design in EMoEA has better Hypervolume （HV） and Inverted Generational Distance （IGD） metrics compared to the original multi-objective evolutionary algorithm， and compared to Non-dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） and Hybrid Jaya （HJaya） algorithm， EMoEA achieves advantages in both HV and IGD metrics with faster convergence and the optimal objective value on most instances. It can be seen that EMoEA has better performance， and EMoEA can solve MFJGSP-STVS effectively， providing high-quality scheduling schemes for enterprises.