In this paper, we address the challenge of optimizing resilience in IT architectures, where increasing interconnection and complexity make traditional cybersecurity insufficient to ensure service continuity. We propose an ontology-based framework that formalizes resilience across three components robustness, adaptability, and recovery by structuring IT systems into components, features, mitigations, techniques, and tools. This ontology enables a quantifiable assessment of resilience and cost, allowing systematic identification of weaknesses and guided improvements. We introduce a multi-objective optimization model, expressed as a scalarized function balancing resilience and cost through weighting coefficients, and solved with the PuLP library using the CBC solver. Several optimization scenarios are explored: tool selection under cost constraints, resilience maximization with limited budgets, and incremental improvement in partially resilient architectures. Results demonstrate the antagonistic relation between resilience and cost, while highlighting feasible trade-offs that can guide decision-makers in adapting resilience strategies to organizational needs. The proposed approach not only formalizes resilience evaluation but also provides a decision-support mechanism for gradual and cost-effective enhancement of IT architectures. Finally, we discuss limitations and future extensions, notably the integration of uncertainty, dynamic cost models, and differentiated resilience weights.