Flexible　job　shop　scheduling　is　difficult,　time-consuming　and　cost-effective　due　to　its　variety　and　number　of　pieces.　In　this　paper,　a　multi-objective　flexible　job　shop　scheduling　model　for　machining　was　established　with　the　optimization　objectives　of　maximum　completion　time,　energy　consumption　and　tool　loss,　considering　rework,　preparation　time　of　sequence　and　batch　scheduling　constraints.　A　multi-objective　flexible　job　shop　scheduling　model　considering　energy　consumption　and　tool　loss　was　proposed,　and　a　machine　selection　strategy　was　proposed　to　improve　the　differential　evolution　algorithm.　The　improved　differential　evolution　algorithm　was　applied　to　machining　flexible　job　shop　scheduling.　Compared　with　the　traditional　differential　evolution　algorithm,　the　improved　differential　evolution　algorithm　has　the　advantages　of　faster　convergence　speed　and　better　robustness,　and　the　optimized　machine　load　is　more　balanced,　which　can　effectively　solve　the　multi-objective　machining　flexible　job　shop　scheduling　problem,　and　provide　a　good　guidance　for　multi　variety　and　multi　piece　scheduling　tasks　plan.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.