This talk is about the recent advances in evolutionary multi-objective optimization techniques and their applications in autonomous systems. One major challenge for autonomous systems concerns the limited time frame for optimization and decision-making. Decision-making is usually required when we are confronted with conflicting objectives and is in fact a very challenging task even for human decision-makers. This is due to the fact that we first need to find all the possible optimal alternatives and then select the right choice using a decision policy. In this talk, we replace the human decision-maker with an autonomous system and provide novel methodologies for multi-criteria decision-making on a range of scenarios in which the autonomous systems are confronted with conflicting objectives. This will enable such systems to change their (pre-defined) decision policy according to the unforeseen circumstances. This ability can contribute to their applicability in critical missions, such as rescue robotics where the intervention of a human-controller is not always possible. The challenge is not only in finding and selecting the best alternative, but also in acting in a limited timeframe during the mission. One more focus of the talk is on the individual vs. collective decision-making algorithms. We will show that collective learning of a decision policy can help both the individual and the collective to act in an efficient way.