Using cloud computing to execute software processes brings several benefits to software development. In a previous work, we proposed a reference architecture, which treats software processes as workflows and uses cloud computing to execute them. Scheduling the execution in the cloud impacts the execution cost and the cloud resources utilization. Existing workflow scheduling algorithms target business and scientific (data-driven) workflows, but not software processes workflows. In this paper, we adapt three scheduling algorithms for our architecture and propose a fourth one; the Proportional Adaptive Task Schedule algorithm. We evaluate the algorithms in terms of their execution cost, makespan and cloud resource utilization. Our results show that our proposed algorithm saves between 19.74% and 45.78% of the execution cost and provides the best resource (virtual machine) utilization compared to the adapted algorithms while providing the second best makespan.