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Utility grids create an infrastructure for enabling users to consume services transparently over a global network. When optimizing workflow execution on utility grids, we need to consider multiple quality of service (QoS) parameters including service prices and execution time. These optimization objectives may be in conflict. In this paper, we have proposed a workflow execution planning approach using multi-objective evolutionary algorithms (MOEAs). Our goal was to generate a set of trade-off scheduling solutions according to the users QoS requirements. The alternative trade-off solutions offer more flexibility to users when estimating their QoS requirements of workflow executions. Simulation results show that MOEAs are able to find a range of compromise solutions in a short computational time.