A genetic algorithm in combination with a mixed integer linear programming solver has been developed to deal with a bilevel (hierarchical) optimization model that represents the interaction of an aggregator and prosumers in flexibility aggregation. The proposed approach is used to assist the aggregator to define the optimal rewards to be given to residential prosumers to characterize their flexibility in the utilization of their energy resources. The rewards are determined in each generation of the genetic algorithm (upper-level problem) aiming to maximize the aggregator’s profit; the rewards are then fed into the prosumers’ problem (lower-level problem). The flexibility responsiveness of the prosumers is calculated using the mixed integer linear programming model at lower-level problem to reschedule the operation of their energy resources and minimize their cost, which in turn impacts the aggregator’s profit. The aggregator can thus characterize the flexibility of prosumers and define the bids to be traded in the ancillary services market. The illustrative results show the performance of the proposed approach to determine the optimal rewards.