Abstract The uncertain nature of engineering variables and parameters dictates the transition of engineering design from global exploration and deterministic optimization to the uncertainty quantification and probabilistic optimization. Therefore, such optimization processes and algorithmic frameworks emerge as key aspects of engineering design, aiming to derive new solutions to all sorts of products and processes. Nature-inspired computing is one of the main drivers, coupled to the continuously evolving engineering models. In this chapter, several aspects of probabilistic optimization are analyzed from an engineering application perspective to highlight the advances and shortcomings as moving towards the efficient global optimization in probabilistic domains. Moreover, the definition of engineering optimization cases, uncertainty quantification techniques, surrogate modeling, and other common case-related challenges are discussed. Finally, this conceptual analysis focuses mainly on engineering models from the aircraft design field, which can provide different types of engineering cases.