Multimodal transportation can effectively improve the operation efficiency of logistics enterprises and reduce the operating costs, which is one of the future development trends of modern logistics. However, there are many nonlinear constraints on the path planning problems, and the traditional accurate algorithm also faces challenges such as ambiguity, particularity, dynamism and high dimensionality. Given the intelligent advantages of bio-inspired algorithms in simulating biological systems, they are extensive and highly efficient in solving this kind of complex combinatorial optimization problems. This paper studies the bio-inspired algorithms based on multimodal transportation path planning in recent years, and divides it into three categories: swarm intelligent algorithms, evolution algorithms and the physics-based bio-inspired algorithms, summarizes the special background, key features and future research direction, widely compare, analyzes the principle, improvement, advantages and limitations, provides the appropriate bio-inspired algorithms for the special scenarios. Finally, the challenges and future research trends are discussed.