Decisions about the analysis and management of engineering systems are frequently made in the presence of uncertainty. Optimization methods have widespread application in engineering decision-making. Recent advances in the fields of uncertainty quantification, data science, and machine learning provide effective optimization frameworks for handling various challenging situations, e.g., high-dimensional applications, rare and high-impact events in optimization, thereby enhancing the decision-making process. This mini-symposium aims to bring together researchers, academics, and practicing engineers interested in the various forms of optimization under uncertainty for engineering systems. We seek contributions discussing novel optimization algorithms and methods, decision analysis frameworks, and metrics, as well as applications in engineering and science. Areas of interest include, but are not limited to, multi-criteria optimization for engineering systems, robust optimization, performance-based optimization, stochastic optimal control, machine learning-based frameworks for sequential decision-making, optimization for engineering risk management, reduced-order modelling, multi-fidelity formulations, and data-driven optimization.