Decision rules provide an intuitive framework for water resources planning. Having adopted a rule-based plan, decision makers can monitor critical variables to trigger timely adaptation actions when the variables pass their predetermined thresholds. However, establishing a strategy that is comprised of a set of decision rules raises methodological challenges: (i) to identify observable indicators that provide reliable information about current and future change, (ii) to choose suitable statistics to characterize nonstationary time series that are germane to system performance, and (iii) to optimize threshold levels that trigger interventions. We propose a methodology that addresses these methodological challenges whilst explicitly balancing expected risks of water shortages with the costs of intervention in the water supply system. The four-step framework uses a multiobjective evolutionary algorithm to search for and to identify the combinations of indicator-informed decision rules that govern if, when, and what supply options should be included in the water resource system. The rule-based strategies are dynamically tested against an extensive ensemble of future climate and demand scenarios to examine the trade-offs between strategy cost and level of service. The framework is applied to the London water system (England) using regional climate simulations to identify strategic rules for a 60-year planning period. The results demonstrate the utility of the framework, identifying observable indicators and decision thresholds that are used in optimal rule-based planning strategies. In key areas of the solution space, rule-based strategies reduce expected restriction costs on average by 13.1%, and as much as 24.1%, for a given intervention cost.