A　multi-objective　flexible　job　shop　scheduling　model　aiming　at　the　completion　time,　idle　time,　processing　quality　and　machine　tool　energy　consumption　is　established　according　to　the　characteristics　of　machine　tool　components　in　production　such　as　multi-varieties,　small　batch　and　large　production　energy　consumption.　And　a　genetic　algorithm　based　on　intuitionistic　fuzzy　set　similarity　(IFS_GA)　is　proposed　to　solve　this　scheduling　model.　The　intuitionistic　fuzzy　set　similarity　value　is　used　as　the　fitness　value　to　lead　the　evolution　of　the　algorithm.　The　crowd　distance　is　used　to　trim　the　external　files　to　improve　the　diversity　of　the　population.　In　order　to　improve　the　quality　of　the　initial　population,　a　weight-based　heuristic　rule　is　proposed.　A　new　chromosome　cross　method　is　presented　to　improve　the　searching　ability　of　the　algorithm.　The　leader　is　selected　by　the　intuitionistic　fuzzy　set　similarity　value　to　guide　the　cross.　In　the　feasible　Pareto　optimal　solution,　the　solution　with　the　highest　similarity　value　of　the　intuitionistic　fuzzy　set　is　selected　as　the　most　satisfactory　solution.　The　proposed　algorithm　is　tested　with　the　verification　methods　of　example　simulation,　instance　simulation　and　QUEST　software.　The　results　show　that　the　IFS_GA　is　effective,　and　it　is　better　than　the　NSGAII.　©　2019,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.