Many software design problems have been addressed using search-based approaches. However, software development usually involves domain-specific design decisions and problems, which are not so common in the SBSE literature. For instance, in the case of video-game development, game-play balancing is a crucial task that involves choosing parameter values for many of the mechanics of the game. This paper addresses the problem of automatically generating balanced decks for a popular card-based game named Top Trumps using a multi-objective evolutionary algorithm. Specifically, three objective functions are defined, some of which involve the simulation of matches by confronting bots with different gaming strategies. The results of the experiments performed, show that the approach can generate decks without silver-bullet cards, for which better strategies usually lead to win, and for which games between players which similar strategies lead to close matches. Finally, an analysis of the impact of game parameters on the objective functions allowed the identification of interesting relationships and bounds on those parameters for a proper use of the algorithm and the objective functions.