Subgraph Mining aims to find frequent, descriptive and interesting subgraphs in a graph database. Usually, this search involves simple user-defined thresholds and is only driven by a single-objective. In this paper, we propose an Evolutionary Multiobjective Optimization algorithm, called MOEP-SO, to mine subgraphs from graph-represented data by maximizing two objectives, support and size of the subgraphs. Experimental results on synthetic and real-life graph-based datasets validate the utility of the proposed methodology when benchmarked against classical single-objective methods and their previous, non-evolutionary multiobjective extensions.