As environmental problems increase, green development has become an indispensable part of the modern logistics system. Therefore, to help the logistics industry achieve the green development goals of energy saving and emission reduction, we studied the green logistics vehicle routing problem. Considering that the Internet of Things (IoT) plays an essential role in logistics scheduling, we constructed a Smart-IoT-based multi-objective green logistics vehicle scheduling model. This model guarantees the accuracy of vehicle-related information in all aspects of dispatch through IoT sensor technologies such as Global Positioning System (GPS) sensors, load sensors, and radio frequency identification (RFID) sensors and considers practical dispatch constraints such as dynamic vehicle carbon emission and physical constraints on heterogeneous vehicles. Furthermore, to address problems such as the poor convergence accuracy of the traditional path planning algorithm, we designed an improved path optimization algorithm by introducing the Levy flight strategy and simulated annealing mechanism in the bald eagle search algorithm to improve the search space and convergence speed of the algorithm. Then we conducted simulation experiments based on the scheduling task of a real logistics company. We also compared the designed algorithm horizontally with cutting-edge algorithms such as the sparrow search algorithm and crow search algorithm to verify the feasibility of the designed model and algorithm. Our results provide a practical reference for helping the logistics industry achieve green development.