With　the　development　of　heterogeneous　distributed　computing　environment,　workflow　application　scheduling　has　become　an　important　and　challenging　problem　while　Quality　of　Service　(QoS)　guarantees　are　ensured　for　science　workflows.　In　this　paper,　we　first　introduce　an　aggregation　measure　factor　to　balance　the　execution　time　and　cost　of　workflow　applications.　Then,　we　propose　an　aggregation　measure　factor-based　scheduling　algorithm　(AFSA)　for　workflow　applications　in　a　heterogeneous　distributed　environment.　The　proposed　algorithm　through　allocating　the　sub-budget　and　sub-deadline　for　each　task　to　choose　available　processors　takes　into　account　of　the　budget　and　deadline　aggregation　to　select　the　processor　for　the　science　workflows.　Furthermore,　we　introduce　both　a　planning　success　rate　and　normalized　deadline　(ND)　as　performance　metrics　to　evaluate　workflow　application　scheduling　algorithms.　Furthermore,　we　use　both　a　randomly　generated　data　set　and　a　real-world　workflow　data　set　in　our　experiments　for　the　performance　evaluation.　Moreover,　our　experimental　results　demonstrate　that　the　proposed　AFSA　has　a　higher　balance　factor　and　an　almost　equal　or　higher　planning　success　rate　under　different　workflow　application　structures　compared　to　the　existing　algorithms,　BHEFT,　HBCS,　WMFCO,　and　DBCS.