‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node ordering‎, ‎more effective node ordering inference methods are needed‎. ‎In this paper‎, ‎based on the fact that the parent and child variables are identified by estimated Markov Blanket (MB)‎, ‎we first estimate the MB of a variable using Grow-Shrink algorithm‎, ‎then determine the candidate parents of a variable by evaluating the conditional frequencies using Dirichlet probability density function‎. ‎Then the candidate parents are used as input for the K2 algorithm‎. ‎Experimental results for most of the datasets indicate that our proposed method significantly outperforms previous method‎.