Multiple Query Optimization (MQO) is a technique for processing a batch of queries in such a way that shared tasks in these queries are executed only once, resulting in significant savings in the total evaluation. The first phase of MQO requires producing alternative query execution plans so that the shared tasks between queries are identified and maximized. The second phase of MQO is an optimization problem where the goal is selecting exactly one of the alternative plans for each query to minimize the total execution cost of all queries. A-star, branch-and-bound, dynamic programming (DP), and genetic algorithm (GA) solutions for MQO have been given in the literature. However, the performance of optimal algorithms, A-star and DP, is not sufficient for solving large MQO problems involving large number of queries. In this study, we propose an Integer Linear Programming (ILP) formulation to solve the MQO problem exactly for a large number of queries and evaluate its performance. Our results show that ILP outperforms the existing A-star algorithm.