This research develops a fuzzy multiobjective optimal model for train stopping scheduling which integrates the train stopping schedule and the operations plan. The objectives of the model are the operator's operating costs, and the user's travel time loss. To make the model solved efficiently, this research develops heuristic algorithms by using a compensatory fuzzy sets aggregation operator to obtain the optimal solution and the lower bounds for the objective. The empirical research is conducted for the high speed rail system. The difference between the lower bounds of operating costs estimated by the heuristic algorithms and that of the optimal solution is less than 1.4%. The lower bounds of frequency and vehicle fleets obtained by the heuristic algorithms are closed to the optimal solution. The results indicate that the heuristic algorithms have practical advantages.