With　the　wide　adoption　of　cloud　computing,　the　scientific　applications　are　migrated　to　cloud　for　execution.　The　complex　structure　of　scientific　applications　bring　challenges　to　optimization　scheduling　scientific　applications　across　multiple　heterogeneous　clouds.　In　this　paper,　the　directed　acyclic　graphs　(DAGs)　are　adopted　to　represent　scientific　applications,　which　have　different　priorities　in　the　process　of　scheduling.　We　propose　a　dynamic　multi-cloud　priority　list　scheduling　algorithm　(DMPLS),　combining　with　workloads　preemptive　strategy　and　feedback　mechanism　to　schedule　scientific　applications　in　time.　Our　algorithm　regulates　the　workloads　scheduling　dynamically　based　on　the　updated　information　about　the　actual　workloads　execution　time.　The　experimental　results　show　that　the　proposed　algorithm　reduces　the　average　time　to　complete　the　applications　compared　with　First-Come-First-Service　and　Round-Robin　algorithm.　Moreover,　the　advantage　of　the　DMPLS　algorithm　is　more　significant　under　the　severe　resources　confliction　situations.