This paper proposes a novel hybrid approach for learning Bayesian networks from incomplete data in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation-Maximization (EM) algorithm. The new algorithm can overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the data sets generated from several benchmark networks illustrate that the new algorithm has better performance than some state-of-the-art algorithms. We also apply the approach to a data set of direct marketing and compare the performance of the discovered Bayesian networks obtained by the new algorithm with the networks generated by other methods. In the comparison, the Bayesian networks learned by the new algorithm outperform other networks.