In this paper, an incremental method for learning Bayesian networks based on evolutionary computing, IEMA, is put forward. IEMA introduces the evolutionary algorithm and EM algorithm into the process of incremental learning, can not only avoid getting into local maxima, but also incrementally learn Bayesian networks with high accuracy in presence of missing values and hidden variables. In addition, we improved the incremental learning process by Friedman et al. The experimental results verified the validity of IEMA. In terms of storage cost, IEMA is comparable with the incremental learning method of Friedman et al, while it is ore accurate.