The problem of automatically selecting simulation models for autonomous agents depending on their current intentions and beliefs is considered in this paper. The intended use of the models is for prediction, filtering, planning and other types of reasoning that can be performed with simulation models. The parameters and model fragments of the resulting model are selected by formulating and solving a hybrid constrained optimization problem that captures the intuition of the preferred model when relevance information about the elements of the world being modelled is taken into consideration. A specialized version of the original optimization problem is developed that makes it possible to solve the continuous subproblem analytically in linear time. A practical model selection problem is discussed where the aim is to select suitable parameters and models for tracking dynamic objects. Experiments with randomly generated problem instances indicate that a hillclimbing search approach might be both efficient and provides reasonably good solutions compared to simulated annealing and hillclimbing with random restarts.