Railway infrastructure maintenance is of fundamental importance in order to ensure a good service in terms of punctuality, safety and efficiently operation of trains on railway track and also for passenger comfort. Track maintenance covers a large amount of different activities such as inspections, repairs, and renewals. In this paper, we address the NP-hard problem of scheduling the preventive railway maintenance activities in order to minimise the overall cost of these activities. Given the complexity of the problem, we propose two meta-heuristics, a variable neighbourhood search (VNS), and an ant colony optimisation (ACO) based on opportunities to deal with this problem. Then, we develop a hybrid approach combining ACO with VNS. The performance of our proposed algorithms is tested by numerical experiments on a large number of randomly generated instances. Comparisons with optimal solutions are presented. The results show the effectiveness of our proposed methods.