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In this paper, we propose a hierarchical approach to path planning that scales well for large crowds. Crowds have become an important research field that presents challenges associated to the high complexity of potential behaviors. In most related work crowds are designed to follow global or local rules that infer intelligent navigation and behavior. Path planning methods can be applied in simulation where it is required to have an exact distribution of the crowd in the environment. However, the simulation is often created offline as path planning algorithms do not scale well with large numbers of agents, if each is considered individually. Our approach provides reliable paths that can be used for environment crowd distribution evaluations, and scales well with a high number of agents to run at interactive from rates for up to a few thousand agents.