As　environmental　contamination　becomes　more　and　more　severe,　enterprises　need　to　consider　optimizing　environmental　criteria　while　optimizing　production　criteria.　In　this　study,　a　multi-objective　green　flexible　job　shop　scheduling　problem　(MO　GFJSP)　is　established　with　two　objective　functions:　the　makespan　and　the　carbon　emission.　To　effectively　solve　the　MO　GFJSP,　an　improved　chimp　optimization　algorithm　(IChOA)　is　designed.　The　proposed　IChOA　has　four　main　innovative　aspects:　1)　the　fast　non-dominated　sorting　(FDS)　method　is　introduced　to　compare　the　individuals　with　multiple　objectives　and　strengthen　the　solution　accuracy.2)　a　dynamic　convergence　factor　(DCF)　is　introduced　to　strengthen　the　capabilities　of　exploration　and　exploitation.　3)　the　position　weight　(PW)　is　used　in　the　individual　position　updating　to　enhance　the　search　efficiency.4)　the　variable　neighborhood　search　(VNS)　is　developed　to　strengthen　the　capacity　to　get　out　of　–　escape　the　local　optimum.　By　executing　abundant　experiments　using　20　benchmark　instances,　it　was　demonstrated　that　the　developed　IChOA　is　efficient　to　solve　the　MO　GFJSP　and　effective　for　reducing　carbon　emission　in　the　flexible　job　shop.　©　2024　–　IOS　Press.　All　rights　reserved.