This article addresses the task assignment problem in IT service companies, where managers must balance profitability with compliance to customer-defined deadlines. We propose a bi-objective mixed-integer linear programming model that simultaneously maximizes company profit and minimizes deviations from service-level agreements (SLAs). To solve this problem, three exact multiobjective optimization methods were applied and compared: Weighted Sum, classical Epsilon Constraints, and Augmented Epsilon Constraints. Numerical experiments highlight the superiority of the Augmented Epsilon Constraints method, which produced high-quality Pareto fronts (GD = 0, Spread ≈ 0.97) while reducing runtime by more than 97% compared to the classical Epsilon Constraints (16.55 s vs. 767.17 s). In contrast, the Weighted Sum method showed limited performance, capturing only convex portions of the front and yielding poor convergence (GD = 7.5). The results confirm that the augmented variant offers the best balance between accuracy, diversity, and computational efficiency. Beyond methodological contributions, this work provides valuable managerial insights: decision-makers can explicitly evaluate trade-offs between profitability and SLA compliance, supporting strategic and informed resource allocation.