With the rapid expansion of the information on the Internet, recommender systems play an important role in filtering insignificant information and recommend satisfactory items to users. Accurately predicting the preference of users is the first priority of recommendation. Diversity is also an important objective in recommendation, which is achieved by recommending items from the so-called long tail of goods. Traditional recommendation techniques lay more emphasis on accuracy and overlook diversity. Simultaneously optimizing the accuracy and diversity is a multiobjective optimization problem, in which the two objectives are contradictory. In this paper, a multiobjective evolutionary algorithm based on decomposition is proposed for recommendation, which maximizes the predicted score and the popularity of items simultaneously. This algorithm returns lots of non-dominated solutions and each solution is a trade-off between the accuracy and diversity. The experiment shows that our algorithm can provide a series of recommendation results with different precision and diversity to a user.