Uncertainty manifests itself in almost every aspect of decision making. Adaptive and flexible policy design becomes crucial under uncertainty. An adaptive policy is designed to be flexible and can be adapted over time to changing circumstances and unforeseeable surprises. A crucial part of an adaptive policy is the monitoring system and associated pre-specified actions to be taken in response to how the future unfolds. However, the adaptive policymaking literature remains silent on how to design this monitoring system and how to specify appropriate values that will trigger the pre-specified responses. These trigger values have to be chosen such that the resulting adaptive plan is robust and flexible to surprises in the future. Actions should be neither triggered too early nor too late. One possible family of techniques for specifying triggers is optimization. Trigger values would then be the values that maximize the extent of goal achievement across a large ensemble of scenarios. This ensemble of scenarios is generated using Exploratory Modeling and Analysis. In this paper, we show how optimization can be useful for the specification of trigger values. A Genetic Algorithm is used because of its flexibility and efficiency in complex and irregular solution spaces. The proposed approach is illustrated for the transitions of the energy system towards a more sustainable functioning which requires effective dynamic adaptive policy design. The main aim of this paper is to show the contribution of optimization for adaptive policy design.