Under the “dual carbon” goals, the optimization of ride-hailing fleet scheduling plays a pivotal role in the low-carbon development of the transportation industry. This paper takes into account low-carbon factors and proposes a multi-objective scheduling model that aims to simultaneously minimize generalized cost and maximize overall demand density benefit. A tailored Greedy-NSGA-II algorithm is designed, which improves the traditional NSGA-II by adopting a novel initial population generation mechanism and a novel repair mechanism. Real-world case studies reveal significant environmental and economic benefits of the proposed multi-objective model. The designed algorithm performs better than the traditional NSGA-II algorithm. The impact analysis provides valuable insights for ride-hailing platforms in formulating operational strategies that promote low-carbon transformation of urban transportation.