AbstractStreet sweeping is used in urban municipalities worldwide but requires a great deal of planning for large‐scale implementation. Municipalities typically make use of the macro‐ and microapproach by using operational areas in which routes are assigned, but creating operational areas without an understanding of the expected workload, number of routes, and the travel distance to and from the depot may lead to increased statistics. In this paper, a combination of heuristic approaches is used to assign street‐sweeping areas on the macroscale and generate street‐sweeping routes on the microscale. For the area assignment, a two‐stage cluster approach is proposed, making use of the weighted k‐means algorithm and differential evolution. For the route optimization, a three‐phase augment merge algorithm is used to create initial solutions, the u‐turns are minimized with a modified version of Hierholzer's algorithm and tabu search, and the remaining u‐turns are removed using a forward‐searching ant colony optimization. A case study in The City of Oshawa, Canada, was used to verify the proposed methodology, and all metrics were theoretically improved.