The explicit evaluation mode of interactive genetic algorithms(IGAs) often brings user fatigue, which greatly limits the performance of IGAs in exploration. Therefore, an IGA with an implicit evaluation mode is proposed based on the interactive actions performed by the user and the conditional preference nets(CP-nets). Firstly, the model of those possible actions is built, and the CP-nets adopted to approximate to the preference of the user are constructed according to few interactive actions. Then, the CP-nets model is adopted to estimate the assignments of those individuals not evaluated by the user, and the evolution process is successfully conducted based on the estimated fitness to assist the user finding his/her interested solution as early as possible. The proposed algorithm is applied to a personalized search for books, and the results show the effectiveness of the proposed algorithm.