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ABSTRACT In recent years, free‐floating bike‐sharing systems have rapidly expanded worldwide, emerging as an essential component of the “last kilometre” of public transportation. While these bikes promote eco‐friendly travel and daily convenience, they also introduce a series of social management challenges. Despite the efforts of numerous cities in China, substantial opportunities remain to better balance supply and demand across temporal and spatial dimensions, as well as to optimize the bike dispatching process, especially in large‐scale scenarios. This research proposed a spatial optimization model of free‐floating bike sharing that considers regionalization and routing simultaneously, coupled with an enhanced Non‐dominated Sorting Genetic Algorithm‐II (Regionalization‐Routing‐NSGA‐II, R 2 ‐NSGA‐II). The model adopted a multi‐stakeholder perspective, focusing on three key factors closely related to real‐world scenarios: supply–demand satisfaction, dispatching time, and workload, which could enhance the feasibility of dispatching strategies. Furthermore, the effectiveness of the R 2 ‐NSGA‐II model was validated through a successful case study in Shenzhen, demonstrating its capability and value in providing actionable solutions for the challenges faced by free‐floating bike‐sharing systems. Finally, the study also acknowledged its limitations, suggesting potential avenues for future research.
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