Collaborative　resource　scheduling　between　edge　terminals　and　cloud　centers　is　regarded　as　a　promising　means　of　effectively　completing　computing　tasks　and　enhancing　quality　of　service.　In　this　paper,　to　further　improve　the　achievable　performance,　the　edge　cloud　resource　scheduling　(ECRS)　problem　is　transformed　into　a　multi-objective　Markov　decision　process　based　on　task　dependency　and　features　extraction.　A　multi-objective　ECRS　model　is　proposed　by　considering　the　task　completion　time,　cost,　energy　consumption　and　system　reliability　as　the　four　objectives.　Furthermore,　a　hybrid　approach　based　on　deep　reinforcement　learning　(DRL)　and　multi-objective　optimization　are　employed　in　our　work.　Specifically,　DRL　preprocesses　the　workflow,　and　a　multi-objective　optimization　method　strives　to　find　the　Pareto-optimal　workflow　scheduling　decision.　Various　experiments　are　performed　on　three　real　data　sets　with　different　numbers　of　tasks.　The　results　obtained　demonstrate　that　the　proposed　hybrid　DRL　and　multi-objective　optimization　design　outperforms　existing　design　approaches.