© 2023 Elsevier B.V.The traction system utilizes the significant part of the energy used by a train. Enhancing driving strategies has the potential to significantly increase energy efficiency. This manuscript outlines a train speed profile optimization strategy and for this, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques have been preferred. The goal of proposed method is to determine the optimal speed profile that uses the least amount of energy at each interstation. First, a train motion simulation model (TMSM) was modeled in Matlab considering the Istanbul M3 Metro Line's route, vehicles and operating constraints. A driving test was employed to validate the simulation. Therefore, the simulated running times, energy consumption and regenerative braking energy differ from the measured ones by an average of 1.24%, 1.70% and 4.4%, respectively. Then, using three case studies and two different strategies, speed profile optimization was carried out with and without comfort constraint. Finally, a real train was operated on the M3 Line with a driver using the optimal speed profiles discovered through simulation. Consequently, it has been demonstrated that the proposed strategy can produce energy savings of 21.38% and 30% in GA and 22.11% and 30.29% in PSO for with and without comfort constraint, respectively.