The　combination　of　unmanned　aerial　vehicles　(UAVs)　and　mobile　edge　computing　(MEC)　technology　breaks　through　the　limitations　of　traditional　terrestrial　communications.　The　effective　line-of-sight　channel　provided　by　UAVs　can　greatly　improve　the　communication　quality　between　edge　servers　and　mobile　devices　(MDs).　To　further　enhance　the　Quality-of-Service　(QoS)　of　MEC　systems,　a　multi-UAV-enabled　MEC　system　model　is　designed.　In　the　proposed　model,　UAVs　are　regarded　as　edge　servers　to　offer　computing　services　for　MDs,　aiming　to　minimize　the　average　task　response　time　by　jointly　optimizing　UAV　deployment　and　computation　offloading.　Based　on　the　problem　definition,　a　two-layer　joint　optimization　method　(PSO-GA-G)　is　proposed.　First,　the　outer　layer　utilizes　a　Particle　Swarm　Optimization　algorithm　combined　with　Genetic　Algorithm　operators　(PSO-GA)　to　optimize　UAV　deployment.　Next,　the　inner　layer　adopts　a　greedy　algorithm　to　optimize　computation　offloading.　The　extensive　simulation　experiments　verify　the　feasibility　and　effectiveness　of　the　proposed　PSO-GA-G.　The　results　show　that　the　PSO-GA-G　can　achieve　a　lower　average　task　response　time　than　the　other　three　baselines.