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Studying biological networks helps to gain a better understanding of cellular behaviors. One of the prominent models to study complex interactions in biological networks is the Nested Effects Model NEM. Based on the Nested Effects Model, we propose two methods for inferring signaling pathways from interventional data. In the first method, we search the space of all feasible solutions with an evolutionary approach to maximize a standard Bayesian score. In the second method, sub-models are constructed with informative features and then combined using an averaging method to make the analysis of larger networks computationally possible. We tested our proposed methods in various noise levels on real and artificial networks with different sizes. The networks constructed by our method have a higher level of accuracy compared to the networks inferred by the triplets method introduced by Markowetz. Moreover, our results show a high level of robustness.