This paper reviews suitable techniques of interactive and preference-based evolutionary multi-objective algorithms to achieve feasible solutions in Pareto-optimal front. We discuss about possible advantages of collective environments to aggregate consistent preferences in the optimization process. Decision maker can highlight the regions of Pareto frontier that are more relevant to him and focus the search only on those areas previously selected. In addition, interactive and cooperative genetic algorithms work on refining users’ preferences throughout the optimization process to improve the reference point or fitness function. Nevertheless, expressing preferences from a unique or small group of decision makers may raise unilateral choices issues and pour hints in terms of search parameter. Supported by a large group of human interaction, collective intelligence is suggested to enhance multi-objective results and explore a wider variety of answers.