In this chapter, we discussed multiobjective design optimization, in which more than one objective is being minimized simultaneously. We started with basic concepts and Pareto optimality, and then we used a simple pyramid example to illustrate the essential points of multiobjective design optimization, especially the Pareto front in the criterion space. We introduced major solution techniques in four categories: methods with a priori articulation of preferences criteria, a posteriori articulation of preferences, no articulation of preferences, and multiobjective genetic algorithms. We pointed out the pros and cons of methods in individual categories and used simple examples for illustration. We also revisited the topic of decision-based design using decision theories, including utility theory and game theory. We reviewed the topic from the context of MOO problems and compared the decision methods with those of conventional MOO solution techniques. We pointed out that the solutions obtained using decision theories are subsets of the Pareto front. However, game theory supports a broader and practical design scenario that involves multiple designers or teams making design decisions simultaneously and sequentially. We reviewed commercial and academic codes for multiobjective design optimization and discussed their pros and cons.