Curiosity is an inherent characteristic of the animal instinct, which stimulates the need to obtain further knowledge and leads to the exploration of the surrounding environment. In this document we present a computational curiosity model, which aims at simulating that kind of behavior on artificial agents. This model is influenced by the two main curiosity theories defended by psychologists – Curiosity Drive Theory and Optimal Arousal Model. By merging both theories, as well as aspects from other sources, we concluded that curiosity can be defined in terms of the agent’s personality, its level of arousal, and the interest of the object of curiosity. The interest factor is defined in terms of the importance of the object of curiosity to the agent’s goals, its novelty, and surprise. To assess the performance of the model in practice, we designed a scenario consisting of virtual agents exploring a tile-based world, where objects may exist. The performance of the model in this scenario was evaluated in incremental steps, each one introducing a new component to the model. Furthermore, in addition to empirical evaluation, the model was also subjected to evaluation by human observers. The results obtained from both sources show that our model is able to simulate curiosity on virtual agents and that each of the identified factors has its role in the simulation.