This research was devoted to optimising opportunistic tamping scheduling to present a cost-effective approach that considers both preventive and corrective tamping activities. To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.We developed predictive models for the evolution of standard deviation and single defects over time, which were utilised to schedule preventive tamping activities and anticipate potential corrective actions. Additionally, we investigated the impact of both preventive and corrective tamping activities on the values of standard deviation and single defects.A case study on data from the Main Western Line in Sweden demonstrated that the fixed cost of occupying each maintenance window significantly influenced the total tamping cost. Moreover, the maintenance cycle interval notably affected the number of required corrective tamping activities. Specifically, a 3-month interval led to over 50 % fewer corrective tamping activities when compared to a 9-month interval. The results revealed that a 6-month interval achieved a favourable balance between corrective and preventive tamping activities and the total cost in our case study.