In view of the efficiency problem of multi-objective recommender systems, this paper utilized the online and offline separation strategy to construct a new frame of recommender system. Aiming at the multi-objective feature of recommender system and current recommendation algorithms' limitations in adaptability, this paper put forward a new multi-objective recommendation algorithm based on the hybrid strategy. Firstly, the algorithm did weighted mix of multiple recommendation algorithms. Secondly, it established a multi-objective optimization model, using the weight sequence as variables and evaluation metrics including F-score, diversity and novelty as objective functions. Then, it optimized the solution through a second version of Strength Pareto Evolutionary Algorithm (SPEA2). Finally, it recommended items to users based on users' shopping preferences and the Pareto set. The experimental results show that: compared with the best single metric sub-recommendation algorithm, the new recommendation algorithm is nearly as well in the F-score, meanwhile increases by 1% in the diversity and increases by 11.5% in the novelty; the distribution of various Pareto solutions of multi-objective forms a dense and neighboring point curve in the solution space. So the recommender algorithm can satisfy the recommend requirements of users with different shopping preferences.