To enhance community-level public transit services and meet residents’ mobility needs, this study proposes a bi-level multi-objective model for route and service frequency planning in shared micro-circulation transit systems. The upper-level model focuses on maximizing passenger ridership while minimizing operator costs, and the lower-level model comprehensively captures the passenger travel process through a network-based elastic demand assignment approach. A customized Non-dominated Sorting Genetic Algorithm II (NSGA-II) specifically adapted to the model is employed to generate a diverse set of Pareto-optimal solutions. A case study demonstrates the model’s effectiveness and the quality of the solutions, revealing a well-distributed Pareto front with notable diversity. Analysis indicates that solutions prioritizing passenger ridership while balancing cost efficiency can lead to the most significant social and economic benefits. Visualization of the selected routes highlights that micro-circulation transit is particularly advantageous in residential and peripheral areas located farther from transit stations. For practical implementation, aligning stop locations and service sequences with actual demand patterns can significantly enhance passenger convenience and simultaneously reduce operational costs.