Squad selection in EA SPORTS FC Ultimate Team (FCUT) poses a complex multi-objective problem where users must balance technical performance, team chemistry, and budgetary constraints. The dynamic nature of the in-game transfer market and the vast number of available player items make this task cognitively demanding, highlighting the need for intelligent decision support tools. This work presents a modular Decision Support System (DSS) that formulates squad building as a constrained multi-criteria optimization problem. Player evaluation is performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which aggregates heterogeneous attributes into interpretable scores based on user-defined weights. To construct complete squads under formation and budget constraints, we employ an Ant Colony Optimization algorithm enhanced with local search, using TOPSIS scores as heuristic guidance and combining them with team chemistry in a composite objective function. The system integrates these components within a scalable web-based architecture and provides explainable recommendations through an interactive assistant. An illustrative example demonstrates that the proposed approach effectively generates competitive squads that align with user preferences, offering a flexible and transparent solution for complex team-building scenarios in dynamic digital environments.