Maintenance of train lines is mandatory to keep the entire system operating. The maintenance schedule keeps the system running; conversely, it can affect the operating schedule. Thus, techniques and algorithms that reduce the scheduling impact are essential while maintaining regular train operations. In this context, this article investigates using a hybrid evolutionary algorithm in scheduling to meet both maintenance activities and minimize the impact time of these activities on the calendar. Experiments on real-world data show that the hybrid algorithm is always superior to the used canonical algorithms, presenting the lowest cost for the given case. Moreover, the hybrid approach is more stable, exhibiting a standard deviation of 0.96.