Demand　for　smart　driving　in　cars　spurs　development　of　time-sensitive　networks　in　vehicles.　Traffic　scheduling　mechanism　is　the　core　mechanism　of　time-sensitive　networks,　which　realize　latency　and　bandwidth　requirements　for　different　types　of　traffic　flows　by　scheduling　the　order　and　time　of　data　frames　in　the　network.　To　address　the　problems　of　poor　performance　and　easy　to　fall　into　local　optimal　solutions　of　existing　genetic　traffic　scheduling　algorithms,　we　propose　an　algorithm　combining　variable　neighborhood　search　and　population　perturbation　in　genetic　algorithm,　which　takes　the　queuing　delay　generated　during　traffic　scheduling　as　an　adaptive　function,　and　improves　the　adaptation　of　individuals　through　iteration　of　population　to　obtain　the　optimal　data　frame　transmission　for　traffic　scheduling　The　optimal　data　frame　transmission　order　for　traffic　scheduling　is　obtained　by　iterating　the　population　to　improve　individual　adaptation.　The　algorithm　is　demonstrated　to　have　better　performance　through　simulation　comparison　experiments.　©　2023　IEEE.