Neighborhood based collaborative filtering is a popular ap- proach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the prob- lem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage.