This article proposes a methodology for developing optimal transit networks (route structures and headways) that minimizes transit transfers and total user cost while maximizing service coverage, given information on transit demand, transit fleet size, and the street network of the transit service area. The research provides an effective mathematical computational tool with minimal reliance on heuristics. The methodology includes representation of transit route networks and solution search spaces, objective functions representing total user cost and unwillingness of users to make transfers, and a global search scheme based on simulated annealing. The methodology has been tested with published solutions to benchmark problems and has been applied to a large-scale realistic network optimization problem in Miami-Dade County, Florida.