This chapter focuses on Multi-Objective Optimization (MOO), where problems require the simultaneous optimization of two or more, often conflicting, objectives. To this end, we first introduce the concept of Pareto dominance and the Pareto front, representing the set of optimal trade-off solutions among the available objectives. MOO techniques are then broadly categorized into two types. First, a priori methods are presented, where decision-maker preferences are incorporated before optimization. Examples include the Weighted Sum method, the \(\epsilon \) -Constraint method, Goal Programming, and Utility Functions. Second, a posteriori methods are discussed, which generate a set of Pareto-optimal solutions for subsequent selection by the decision-maker. Examples of these techniques include Multi-Objective Evolutionary Algorithms (MOEAs) such as NSGA-II, NSGA-III, SPEA2, MOEA/D, and MOPSO. The chapter also explains the role of Multi-Criteria Decision-Making (MCDM) techniques in understanding, selecting, and ranking solutions from the Pareto front. Similar to the previous chapter, each strategy’s core principles, mechanisms, and key parameters are explained, balancing technical depth with conceptual overview.