In this paper we propose an Ant System (AS) SimU-AS and an Ant Colony System (ACS) SimU-TACS, both used to solve a planning problem in the Health Simulation Center SimUSanté. This center proposes up to 500 different training sessions based on simulation learning for healthcare staff. The data, constraints and objective of the SimUSanté problem, which is close to the academic Curriculum-Based Courses Timetabling (CB-CTT) Problem, are detailed. To guide ants toward promising areas of the search space, SimU-AS and SimU-TACS rely on several heuristics that aim to reduce the makespan of each session. Moreover, SimU-TACS combines features like the -greedy strategy, the tabu memory system and restarts to improve the search. SimU-AS and SimU-TACS were compared to SimU-VNS, a Variable Neighborhood Search, to SimU-MMAS, a hybridized Max-Min Ant System, and to optimal solutions when achievable. They were also compared to the open source KHE solver by relaxing the precedence constraints. Experiments show that SimU-TACS outperforms all other methods, even for the largest instances, without violating any hard constraints. Moreover it provides optimality for 31/48 instances.