In　this　paper,　we　propose　a　niching　Pareto　ant　colony　optimization　(NPACO)　algorithm　to　solve　the　bi-objective　pathfinding　problem.　First,　based　on　a　planar　navigable　data　model,　three　different　searching　area　restricted　methods　are　proposed　and　compared.　In　addition,　a　node　simplification　strategy　is　introduced　to　simplify　nodes　that　exist　in　network　branch　loops,　eliminating　the　redundant　search　time　in　the　branch　loops.　Afterward,　we　propose　the　elitist　ants　and　weakened　strategy　for　an　ACO　to　overcome　the　problem　caused　by　the　impact　of　accumulated　pheromone　on　the　suboptimal　path　and　apply　the　strategy　to　a　PACO　for　urban　city　pathfinding.　Finally,　the　niching　method　is　adopted　to　simultaneously　locate　and　maintain　multiple　optimal　solutions　to　increase　search　robustness.　The　experimental　results　show　that　the　NPACO　with　a　restricted　and　simplified　search　area　returns　a　Pareto　optimal　solution　set　that　is　uniformly　distributed　along　the　Pareto　frontier　with　low　computational　complexity.