The cloud emerges as an appropriate infrastructure for executing Scientific Workflows (SWfs). However, it is dicult to execute some SWfs at one cloud site because of geographical distribution of data and computing resources among different cloud sites. Therefore, the major problem is to be able to execute a SWf in a multisite cloud, while typically reducing execution time and monetary costs. In this talk, we propose a multisite SWf scheduling approach, i.e. ActGreedy, which addresses this problem. ActGreedy is based on a multi-objective cost model and a Virtual Machine (VM) provisioning algorithm, i.e. Single Site VM Provisioning (SSVP). We present an experimental evaluation, based on the execution of the SciEvol SWf, a molecular evolution reconstruction workflow, in Microsoft Azure cloud. The experiment results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms). In addition, the experiments show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach.