Auto battlers are a recent genre of online deck-building games where players choose and arrange cards that then compete against other players' cards in fully-automated battles. As in other deck-building games, such as trading card games, designers must balance the cards to permit a wide variety of competitive strategies. We present Ludus, a framework that combines automated playtesting with global search to optimize parameters for each card that will assist designers in balancing new content. We develop a sampling-based approximation to reduce the playtesting needed during optimization. To guide the global search, we define metrics characterizing the health of the metagame and explore their impacts on the results of the optimization process. Our research focuses on an auto battler game we designed for AI research, but our approach is applicable to other auto battler games.