A decision policy chooses an outcome dependent on given input parameters. Policies can adequately be represented by production rules, which are at the heart of modern business rule management systems. Classic ways of policy design are rule authoring by experts or learning from data. In this paper, we show that policies can also be derived from a model consisting of constraints and preferences. We can design a policy that respects the given preferences by solving a particular combinatorial Pareto-optimization problem. We consider a combined parameter and decision space and introduce a rule for each Pareto-minimal infeasible lower bound in this space. The approach gives interesting insights in the relationships between combinatorial optimization under preferences and rule-based decision making.