With　the　rapid　development　of　mobile　technology,　the　demands　of　mobile　applications　for　computational　resources　are　increasing.　Limited　by　some　factors,　the　computational　capacities　of　mobile　devices　(MDs)　cannot　meet　mobile　application　requirements.　Mobile　edge　computing(MEC)　has　emerged　in　this　context　and　has　brought　innovation　into　the　working　mode　of　traditional　cloud　computing.　By　deploying　edge　servers　at　the　network　edge,　the　computation　resources　of　cloud　center　are　sinking,　and　the　enrich　computational　resources　of　edge　servers　make　up　for　the　lack　of　MDs.　As　a　specific　form　of　edge　server,　cloudlet　has　been　widely　concerned　by　academia　and　industry　in　recent　years.　However,　the　existing　works　mainly　focus　on　the　computation　offloading　of　simple　tasks　under　the　condition　of　fixed　cloudlet　positions　and　ignore　the　impact　of　cloudlet　deployment　scheme　and　data　dependence　among　components　of　workflow　applications　(WAs)　on　the　result　of　computation　offloading.　In　this　paper,　the　cloudlet　deployment　for　WAs　in　a　MEC-wireless　metropolitan　area　network　(WMAN)　is　formulated.　We　propose　a　encode　library-enabled　particle　swarm　optimization　using　genetic　algorithm　operators　(EL-PSOGA)　algorithm　to　optimize　the　execution　end　time　of　all　WAs.　Numerical　results　show　that　our　algorithm　can　effectively　reduce　the　execution　end　time　of　all　WAs　in　the　system　compared　with　several　benchmark　algorithms.