Pure　electric　bus　has　become　an　important　option　for　the　electric　transformation　of　vehicles　due　to　its　low-carbon,　energy-saving,　and　environmental　protection　characteristics.　However,　pure　electric　buses　still　face　challenges　such　as　performance　degradation　under　low-temperature　conditions　and　reduced　mileage　due　to　battery　aging　in　actual　operation.　The　mixed　use　of　fuel　buses　and　pure　electric　buses　in　operation　helps　to　improve　the　performance　degradation　of　pure　electric　buses　in　specific　scenarios,　and　to　enhance　the　efficiency　and　service　quality　of　bus　operation.　This　paper　proposes　a　segmented　optimization　model　for　bus　timetables　considering　the　dynamic　operation　characteristics　of　buses.　With　the　optimized　frequency　as　input,　a　bus　fleet　scheduling　planning　compilation　model　is　developed　under　mixed　bus　operation　conditions.　An　improved　genetic　algorithm　is　designed　to　solve　the　model.　Taking　the　bus　routes　in　Beijing　as　an　example,　the　case　studies　were　conducted　in　different　typical　operational　scenarios　such　as　single-line　operation,　remote　charging,　and　regional　centralized　scheduling　to　verify　the　applicability　and　optimization　effect　of　the　model　under　differentiated　operational　scenarios.　The　results　indicate　that　compared　to　local　charging　scenarios,　operational　costs　increased　by　5.15%　and　the　number　of　operating　vehicles　increased　by　5.88%　in　remote　charging　scenarios.　In　the　regional　centralized　scheduling　scenario　where　multiple　routes　are　jointly　scheduled,　operational　costs　decreased　by　4.68%　compared　to　single-line　operation　scenarios.　Under　the　condition　of　given　bus　types　proportion　threshold,　the　effectiveness　of　mixed　vehicle　operation　surpasses　single　vehicle　type　operation,　effectively　reducing　operational　costs　and　carbon　emissions.　This　study　provides　a　support　for　public　transport　enterprises　to　create　scientific　and　flexible　electric　bus　operation　scheduling　schemes　based　on　different　operation　scenarios.　©　2024　Science　Press.　All　rights　reserved.