Mobile　edge　computing　(MEC)　necessitates　cost-effective　deployment　for　executing　scientific　workflows　with　different　tasks　and　datasets,　which　provides　computing,　storage　and　network　control　at　the　network　edge.　However,　the　execution　of　scientific　workflows　in　MEC　results　in　heavy　costs　of　data　placement　including　data　transmission　and　data　storage.　Although　there　are　solutions　for　data　placement　in　traditional　cloud　computing,　they　cannot　effectively　respond　to　the　latency-sensitive　property　of　scientific　workflows,　which　leads　to　the　excessive　costs　of　data　placement.　To　cope　with　this　problem,　we　combine　the　advantages　of　MEC　and　cloud　computing　and　propose　a　genetic　algorithm　particle　swarm　optimization　(GAPSO)　based　method　to　explore　the　optimal　strategy　of　data　placement　for　scientific　workflows　in　MEC.　First,　a　unified　model　of　data　placement　is　designed　to　explore　a　cost-effective　strategy,　which　considers　the　different　characteristics　between　MEC　and　cloud　computing　as　well　as　the　impact　of　latency　constraint　on　transmission　costs.　Next,　the　advantages　of　genetic　algorithm　(GA)　and　particle　swarm　optimization　(PSO)　are　integrated　to　optimize　the　proposed　model,　which　utilities　the　fast　convergence　of　PSO　and　the　crossover　and　mutation　operations　of　GA.　Simulations　using　real-world　scientific　workflows　show　the　effectiveness　of　the　proposed　method　for　reducing　data　placement　costs　in　MEC.