To tackle the potentially hard task of defining the reward function in a Markov Decision Process (MDPs), a new approach, called Interactive Value Iteration (IVI) has recently been proposed by Weng and Zanuttini (2013). This solving method, which interweaves elicitation and optimization phases, computes a (near) optimal policy without knowing the precise reward values. The procedure as originally presented can be improved in order to reduce the number of queries needed to determine an optimal policy. The key insights are that (1) asking queries should be delayed as much as possible, avoiding asking queries that might not be necessary to determine the best policy, (2) queries should be asked by following a priority order because the answers to some queries can enable to resolve some other queries, (3) queries can be avoided by using heuristic information to guide the process. Following these ideas, a modified IVI algorithm is presented and experimental results show a significant decrease in the number of queries issued.