To　obtain　better　solution　of　many-objective　permutation　flow-shop　scheduling　problems　(PFSP),　a　genetic　algorithm　based　on　similarity　of　intuitionistic　fuzzy　sets　(SIFS　GA)　is　proposed.　In　this　algorithm,　reference　solution　and　Pareto　solution　are　mapped　into　reference　solution　intuitionistic　fuzzy　sets　and　Pareto　solution　intuitionistic　fuzzy　sets　respectively.　The　similarity　of　intuitionistic　fuzzy　sets　between　two　sets　is　calculated　and　adopted　to　determine　the　quality　of　the　Pareto　solution.　The　similarity　value　of　intuitionistic　fuzzy　sets　is　used　as　the　fitness　value　of　GA　to　guide　the　algorithm　evolution.　Finally,　simulation　experiments　are　carried　out　with　6　CEC　benchmark　examples　and　10　flow　shop　scheduling　test　examples　to　analyze　the　proposed　algorithm.　Experimental　results　show　that　SIFS　GA　can　obtain　better　results　than　other　commonly　used　many-objective　optimization　algorithms,　and　can　effectively　solve　many-objective　flow　shop　scheduling　problems,　especially　in　solving　the　problem　of　large　scale.　©　2019,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.