With the generalization of the Cloud, software providers can distribute their software as a service without investing in large infrastructure. However, without an effective resource allocation method, their operation cost can grow quickly, hindering the profitability of the service. This is the case for BPM as a Service providers that want to handle hundreds of customers with a given quality of service. Since there are variations in the capacity and the number of users, the allocation method must be able to adjust the resource and the allocation of customer on these resources. In this paper we present a cost optimization model and a heuristic based on genetic algorithms to adjust resource allocation to the need of a set of customers with varying BPM task throughput. Experi-mentations using realistic customer loads and cloud resources capacities shows the gain of this method compared to previous approaches.