In a Bayesian Network (BN), a target node is independent of all other nodes given its Markov Blanket (MB). By finding the MB, many problem can be solved directly or indirectly. There exist predominately two different approaches to finding the MB: the score-based and the constraint-based algorithms. We introduce a new Markov Blanket learning algorithm, Hybrid Markov Blanket (HMB) discovery, by combining these two different approaches. Specifically, HMB first employs a score-based method for finding the parents and children (PC) of the target node. HMB then introduces an efficient constraint-based approach to finding target node's spouses without enforcing the symmetry constraint that is required by existing constraint-based methods. In comparison, HMB achieves a better accuracy than the traditional constraint-based approaches and a better efficiency than the existing score-based approaches. In addition, HMB is theoretically proven sound and complete. Empirical results on synthetic and standard MB discovery datasets demonstrate the superior performance of HMB.