Dealing with multiple conflicting objectives in a multi-agent system is challenging, as agents’ interactions complicate decision-making, especially when managing multiple Pareto-optimal fronts. In competitive co-evolutionary frameworks, not only do the objectives of each agent conflict with one another, but the agents’ goals are also at odds. One such application domain is wargame strategy optimization, where the strategies of one agent must adapt based on the moves of opposing agents. Despite advancements in modern warfare, strategy analysis and decision-making are still largely manual, leaving room for great application of computational methods to automate different parts of the system. To address this, we propose a co-evolutionary optimization algorithm that integrates strategy search with interactive decision-making, allowing co-evolving populations to collaboratively identify their respective Pareto-optimal strategies. Central to this approach is a progressive-shrinking method that aligns feasible moves with those previously taken, ensuring smoother transitions. Our framework introduces a novel decision-making strategy using opposition front hypervolume improvement, particularly suited for competitive co-evolutionary contexts, combined with Penalty-based Boundary Intersection selection, to optimize strategy selections. We also examine the influence of various decision-making approaches, shrinking techniques, and parameter settings on the final results. This co-evolutionary framework, combining multi-agent interaction, evolutionary multi-objective optimization, and progressive shrinking, is not only effective for wargame strategy optimization but is also adaptable to other multi-agent conflicting systems.