To meet different needs of users in the recommendation system, an immune clone algorithm is proposed. The problem is modeled as a multi-objective optimization problem to maximize the accuracy and diversity in the recommendation lists. It uses collaborative filtering technology to finish the user classification. Antibody coding and the immune operator that suitable for solving the problem are designed in the algorithm. The results show that the proposed algorithm can effectively obtain the optimal solutions of personalized recommendation, which satisfies with different needs of multiple users at the same time.