The　advances　in　cloud　computing　promote　the　problem　in　processing　speed.　Computing　resources　in　cloud　play　a　vital　role　in　solving　user　demands,　which　can　be　regarded　as　workflows.　Efficient　workflow　scheduling　is　a　challenge　in　reducing　the　task　execution　time　and　cost.　In　recent　years,　deep　reinforcement　learning　algorithm　has　been　used　to　solve　various　combinatorial　optimisation　problems.　However,　the　trained　models　often　have　volatility　and　can　not　be　applied　in　real　situation.　In　addition,　evolutionary　algorithm　with　a　complete　framework　is　a　popular　method　to　tackle　the　scheduling　problem.　But,　it　has　a　poor　convergence　speed.　In　this　paper,　we　propose　a　hybrid　algorithm　to　address　the　workflow　scheduling　problem,　which　combines　deep　reinforcement　algorithm　and　evolutionary　algorithm.　The　solutions　generated　by　deep　reinforcement　learning　are　the　initial　population　in　the　evolutionary　algorithm.　Results　show　that　the　proposed　algorithm　is　effective.