Community　structure　detection　in　complex　networks　contributes　greatly　to　the　understanding　of　complex　mechanisms　in　many　fields.　In　this　article,　we　propose　a　multiagent　evolutionary　method　for　discovering　communities　in　a　complex　network.　The　focus　of　the　method　lies　in　the　evolutionary　process　of　computational　agents　in　a　lattice　environment,　where　each　agent　corresponds　to　a　candidate　solution　to　the　community　detection　problem.　First,　the　method　uses　a　connection-based　encoding　scheme　to　model　an　agent　and　a　random-walk　behavior　to　construct　a　solution.　Next,　it　applies　three　evolutionary　operators,　i.e.,　competition,　crossover,　and　mutation,　to　realize　information　exchange　among　agents　and　solution　evolution.　We　tested　the　performance　of　our　method　using　synthetic　and　real-world　networks.　The　results　show　its　capability　in　effectively　detecting　community　structures.