Variable Ordering (VO) plays an important role when inducing Bayesian Networks (BNs). Previous works in the literature suggest that it is worth pursuing the use of evolutionary strategies for identifying a suitable VO, when learning a Bayesian Network structure from data. This paper proposes a hybrid adaptive algorithm named VOMOS (Variable Ordering Multiple Offspring Sampling) where the new individuals are created using a set of recombination operators (crossover and mutation operators). Experiments performed in datasets revealed that the VOMOS approach is promising and tends to generate consistent and representative BNs.