Train scheduling is one of the most important and complex elements in the planning process for railway operations. However, this task is still done manually at the Taiwan High Speed Rail Company. The plan to add four stations and more than 100 additional daily trains in the near future necessitates the improvement of the quality and efficiency of the company's train scheduling process. This research developed a novel two-stage hybrid method that efficiently automated and optimized the scheduling process for high-speed trains. The first stage applied a genetic algorithm to solve train sequencing problems. The resultant sequence was then sent to the second stage to determine the optimal timetable with linear programming techniques. Results from the linear programming model were sent as feedback to the first stage to determine the overall performance of the timetable by using the fitness function in the genetic algorithm. The timetable gradually evolved toward an optimal solution based on this iterative process. Experimental results demonstrated that this hybrid method not only improved the efficiency of the solution substantially but also provided better timetables compared with the current practice. The proposed method can help highspeed rail companies automate the scheduling process and improve the solution's performance with efficiency.