The　optimization　of　passenger　evacuation　paths　in　subway　stations　under　flood　scenarios　plays　an　important　role　in　improving　evacuation　efficiency　and　ensuring　escape　safety.　Considering　the　impact　of　evacuation　network　failures　such　as　gates　on　path　planning,　a　two-stage　passenger　evacuation　path　optimization　method　under　flood　scenarios　of　subway　stations　is　established　with　the　objectives　of　minimizing　total　evacuation　time,　risk,　and　congestion.　The　black　widow　algorithm　is　proposed　to　optimize　the　BP　neural　network　model　to　predict　the　travel　time　of　passengers　at　nodes,　which　could　improve　the　prediction　accuracy　of　calculating　the　total　evacuation　time.　The　path　optimization　model　is　solved　by　the　NSGA-II　algorithm,　and　the　optimal　Pareto　solution　is　determined　based　on　the　minimum　total　cost.　Taking　a　subway　station　in　Qingdao,　China　as　an　example,　a　passenger　evacuation　simulation　system　under　flood　scenarios　is　built　using　PathFinder　software.　The　effect　of　the　path　optimization　strategy　is　comprehensively　evaluated　through　comparative　experiments.　It　is　found　that　the　overall　evacuation　optimization　degree　could　be　increased　by　18.75%,　by　comparing　the　evacuation　time,　congestion,　and　risk　objectives　under　the　situations　with　and　without　path　optimization　strategies.