To improve the efficiency of large-scale personnel evacuation and reduce casualties after toxic gas leakage accidents, a CFD model and wind direction statistical probability were adopted to simulate the spatiotemporal distribution of toxic gas concentrations. Combined with the facilities and personnel conditions of evacuation sites, a dynamic comprehensive risk measurement method for evacuation sites was proposed. First, based on the differences in dynamic comprehensive risks of evacuation sites, a multi-trip emergency evacuation bus scheduling model with splitable evacuation demand was constructed, with the objectives of minimizing evacuation time and total risk expectation. Then, the augmented weighted Chebyshev method was used to convert the multi-objective model into a single-objective model, and a genetic algorithm integrated with adaptive large neighborhood search was designed for solution, in which destruction and repair operators were adopted to improve the search capability of the algorithm. Finally, the rationality of the model and the effectiveness of the algorithm were verified through numerical example analysis.The results show that the emergency evacuation bus scheduling model with the objectives of minimizing evacuation time and total evacuation risk expectation can generate scheduling schemes that prioritize the evacuation of personnel from high-risk evacuation sites, thereby improving evacuation efficiency and reducing the total risk expectation. Compared with the expected total evacuation risk, evacuation time is more sensitive to changes in the latest time window of evacuation sites, and the number of required buses decreases with the extension of the evacuation time window. In comparison with the traditional genetic algorithm, the genetic algorithm integrated with adaptive large neighborhood search can reduce the objective functions by 4.46% and 2.44%, respectively.