Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein-protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose a framework, Genetic Algorithm enhanced Time Varying Relational Classifier for evolving Heterogeneous Networks (GA-TVRC-Het), to extract the effects of different relationship types in different time periods in the past. These effects are discovered adaptively by utilizing genetic algorithms. A relational classifier is extended as the classification method in order to be able to work with different types of nodes. The proposed framework is tested on two real world data sets. It is shown that using the optimal time effect improves the classification performance to a large extent. It is observed that the optimal time effect does not necessarily follow a certain functional trend, for example linear or exponential decay in time. Another observation is that the optimal time effect may be different for each type of interaction. Both observations reveal the reason why GA-TVRC-Het outperforms methods that rely on a predefined form of time effect or the same time effect for each link type.