Multi-Objective Optimization involves simultaneously optimizing multiple conflicting objectives. Given objective functions f₁(x), f₂(x), ..., fₖ(x), find solutions that represent optimal trade-offs. A solution x* is Pareto optimal if no other solution improves one objective without worsening another. The Pareto front is the set of all Pareto optimal solutions. Solution approaches: (1) weighted sum method - combine objectives into single function; (2) ε-constraint method - optimize one objective while constraining others; (3) goal programming - minimize deviations from target goals; (4) evolutionary algorithms (NSGA-II, MOEA/D). Applications: engineering design, portfolio optimization, supply chain management, and sustainable development where economic, environmental, and social objectives conflict.