Strategic decisions are often complex and multifaceted and involve many different stakeholders with different objectives and priorities. Very often decision-makers (DMs), when confronted with such problems, attempt to use intuitive or heuristic approaches to simplify the complexity until the problem seems more manageable. In this process, important information may be lost, opposing points of view may be discarded, and elements of uncertainty may be ignored. A crucial issue, when dealing with strategic decisions, is the radical uncertainty about the present (e.g. lack or poor quality of information) and also about the future. The latter one addresses the seeming paradox—how can we evaluate the rationality of our decisions today if the most important fact that we know about future conditions is that they are unknowable? In the literature it is mentioned that robustness analysis is a way of supporting strategic decision making when dealing with uncertainties and ignorance. In the present chapter we discuss how facilitated forms of MCDA could tackle different aspects associated with strategic decision making and provide effective support in dealing with robustness of strategic decisions in designing complex strategies with long-term consequences. We finally present three case studies where MCDA approaches were used to tackle strategic decision problems.