With　the　development　of　cloud　computing,　the　coexistence　of　multiple　cloud　service　providers　appears　in　the　current　cloud　market.　Due　to　heterogeneous　instance　types,　different　bandwidths　and　various　price　models　among　multiple　clouds,　it　is　a　challenging　issue　to　schedule　a　deadline-constrained　scientific　workflow　across　multiple　clouds.　Existing　research　for　workflow　scheduling　are　mostly　in　the　traditional　distributed　computing　environment　(such　as　grid),　and　only　a　few　primal　contributions　are　made　in　the　cloud　environment.　This　paper　proposes　a　scheduling　strategy　for　a　deadline-constrained　scientific　workflow　across　multiple　clouds.　In　order　to　minimize　the　execution　cost　of　the　workflow　while　meeting　its　deadline,　our　strategy　utilizes　the　discrete　particle　swarm　optimization　technique,　and　adopts　randomly　two-point　crossover　operator　and　randomly　single　point　mutation　operator　of　the　genetic　algorithm.　Besides,　the　strategy　optimizes　the　performance　for　both　computation　cost　and　data　transfer　cost　across　multiple　clouds.　Our　strategy　is　evaluated　through　well-known　workflows,　and　experimental　results　show　that　it　performs　better　than　other　state-of-the-art　strategies.