Many real-world search and optimization problems are naturally multi-objective. Due to the lack of suitable solution techniques, such problems were artificially converted into a single-objective problem and solved. The difficulty arose because such problems give rise to a set of trade-off optimal solutions, instead of a single optimum solution. It then becomes important to find not just one Pareto-optimal solution, but as many of them as possible. This is because any two such solutions constitute a trade-off among the objectives and users would be in a better position to make a choice when many such trade-off solutions are unveiled. In this talk, we are going to introduce several multi-objective optimization algorithms and their real-world applications.