Replacing conventional buses with electric buses is in line with the concept of sustainable development. However, electric buses have the disadvantages of short driving range and high purchase price. Many cities must implement a semi-electrification strategy for bus routes. In this paper, a bi-level, multi-objective programming model is established for the collaborative scheduling problem of vehicles and drivers on a bus route served by the mixed bus fleet. The upper-layer model minimizes the operation cost and economic cost of carbon emission to optimize the vehicle and charging scheme; while the lower-layer model tries to optimize the crew-scheduling scheme with the objective of minimizing driver wages and maximizing the degree of bus-driver specificity, considering the impact of drivers’ labor restrictions. Then, the improved multi-objective particle swarm algorithm based on an ε-constraint processing mechanism is used to solve the problem. Finally, an actual bus route is taken as an example to verify the effectiveness of the model. The results show that the established model can reduce the impact of unbalanced vehicle scheduling in mixed fleets on crew scheduling, ensure the degree of driver–bus specificity to standardize operation, and save the operation cost and driver wage.

Charging piles in the bus depot provide charging services to multiple electric bus (EB) routes operating in the area. As charging needs may overlap between independently operated routes, EB fleets often have to wait in line for charging. However, affected by the ambient temperature, the length of the waiting time will cause the battery temperature to change at the beginning of each charging, thereby influencing the charging performance and charging time of the battery. To this end, this paper considers the influence of ambient temperature on battery charging performance, and collaboratively optimizes the number of charging piles in the bus depot and the scheduling problem of EB charging. Aiming at minimizing the cost of laying charging piles in bus stations and the charging costs of bus fleets, as well as minimizing the empty time of electric bus fleets and waiting time for charging in queues, a mixed-integer nonlinear programming model is established, and the immune algorithm is used to solve it. At last, an actual bus depot and four EB routes are taken as examples for verification. The results show that by optimizing the charging waiting time of the electric bus at the bus station, the rapid decline in charging performance caused by the sharp drop in battery temperature is avoided. Without increasing the charging cost of the electric bus fleet, the established method reduces the charging pile installation cost, improves the bus depot’s service efficiency, and ensures the punctuality and integrity of the regional bus route operation.