As an efficient logistics mode, multimodal transport can effectively integrate the advantages of various transportation modes and reduce energy consumption and carbon emissions. Aiming at the low-carbon multimodal transport path optimization problem, this paper provides a hybrid optimization method combining genetic algorithm and particle swarm optimization algorithm, establishes a multi-objective optimization model considering transport time, cost and carbon emission, and designs a PSO-GA hybrid algorithm to realize the comprehensive optimization of multimodal transport path. Finally, a numerical example is given to verify the effectiveness and feasibility of the proposed method. The results show that the PSO-GA hybrid algorithm can effectively reduce the transportation cost and carbon emissions of multimodal transportation while ensuring transportation efficiency, and provide theoretical support and practical guidance for the low-carbon development of China’s transportation industry, which will help China set a model of low-carbon development in the field of global transportation and make positive contributions to the global response to climate change.