A spatial co-located event set represents a subset of spatial events whose instances are located in a spatial neighborhood. The discovery of co-evolving spatial event sets involves finding co-located event sets whose spatial prevalence variations over time are similar to a specific query sequence. Mining co-evolving spatial event sets is computationally challenging due to the high computational cost of finding co-located event instances on continuous geographic space, large temporal space and a composite interest measure, i.e., the spatial prevalence time sequence of a co-located event set. We propose a novel method for mining co-evolving spatial event sets. We analyze the proposed algorithm in terms of correctness and completeness, and experimentally evaluate the algorithm.