An ant colony optimization approach for the satellite control resource scheduling problem is presented. Based on the observation that the solution space of the problem is sparse, two pheromone updating methods, i.e., the reinitialize-guidance-updating and current-guidance-updating methods, are proposed to avoid the trapping in local optima. The basic idea of these two methods is to change the distribution of pheromone trails by updating them with a guidance solution once the algorithm stagnates. We compare the proposed algorithm with several other heuristics. The experimental results demonstrate that our approach is competitive in terms of exploration capability of reaching the near-global optimal solution and adaptability to the future situations.