Battery　electric　buses　are　increasingly　applied　and　promoted　in　public　transportation　systems　due　to　their　advantages,　such　as　low　emissions　and　low　noise　levels.　However,　their　limited　driving　range　and　long　charging　time　necessitate　frequent　charging　during　daily　operations,　thus　leading　to　a　new　charging　scheduling　problem.　A　reasonable　charging　schedule　is　of　great　significance　in　reducing　the　construction　cost　of　charging　facilities　and　charging　costs.　However,　current　research　on　optimizing　electric　bus　charging　schedules　typically　assumes　a　linear　relationship　between　charging　time　and　state　of　charge　(SOC),　and　often　neglects　the　comprehensive　optimization　of　charging　schedules　and　charging　station　operations,　resulting　in　poor　scenario　reproduction　and　resource　inefficiencies.　Therefore,　this　paper　further　studied　the　optimization　of　charging　schedules　based　on　a　predefined　set　of　bus　trip　schedules.　A　mixed-integer　programming　model　was　developed　to　minimize　the　total　system　cost　by　optimizing　the　occurrence　periods,　the　start　and　end　times　of　charging,　and　the　schedules　of　the　charging　piles　synchronously.　The　model　also　fully　considers　time-of-use　electricity　pricing　policy,　partial　charging　strategies,　and　the　nonlinear　characteristics　of　battery　charging.　To　solve　the　problem,　this　paper　first　linearized　the　nonlinear　charging　function　of　the　battery　into　a　piecewise　linear　one　and　then　used　the　commercial　solver　Gurobi　to　obtain　the　optimal　solution.　Additionally,　a　tailored　algorithm　was　designed　based　on　the　minimum-cost-maximum-flow　theory　and　the　deficit　function.　Multiple　sets　of　experiments　were　conducted　to　validate　the　effectiveness　of　the　proposed　algorithm　based　on　five　bus　routes　in　Beijing.　The　results,　obtained　through　both　Gurobi　and　the　proposed　optimized　algorithm,　demonstrate　that　the　proposed　algorithm　can　achieve　a　significant　reduction　in　total　system　costs,　ranging　from　28.　34%　to　56.　1%　across　various　scenarios.　These　findings　confirm　the　efficiency　of　the　algorithm　and　its　potential　to　optimize　charging　schedules　effectively.　©　2024　South　China　University　of　Technology.　All　rights　reserved.