Expectation maximization (EM) is a popular algorithm for parameter estimation in situations with incomplete data. The EM algorithm has, despite its popularity, the disadvantage of often converging to local but non-global optima. Several techniques have been proposed to address this problem, for example initializing EM from multiple random starting points and then selecting the run with the highest likelihood. Unfortunately, this method is computationally expensive. In this paper, our goal is to reduce computational cost while at the same time maximizing likelihood. We propose a Genetic Algorithm for Expectation Maximization (GAEM) for learning parameters in Bayesian networks. GAEM combines the global search property of a genetic algorithm with the local search property of EM. We prove GAEM’s global convergence theoretically. Experimentally, we show that GAEM provides significant speed-ups since it tends to select more fit individuals, which converge faster, as parents for the next generation. Specifically, GAEM converges 1.51.5 to 77 times faster while producing better log-likelihood scores than the traditional EM algorithm.