Aiming　at　the　problem　that　the　computing　tasks　requested　by　user　equipments　of　(UEs)　exceed　the　computing　capacity　of　mobile　edge　computing　(MEC)　server　in　the　ground　base　station　(BS),　an　unmanned　aerial　vehicle　(UAV)-assisted　resource　allocation　strategy　is　proposed　in　this　paper.　By　deploying　a　UAV　carried　with　an　MEC　server,　when　the　computing　tasks　requested　by　the　UEs　are　beyond　the　computing　capacity　of　the　ground　BS　MEC　server,　the　UEs　can　offload　the　extra　computing　tasks　to　the　UAV.　The　resource　allocation　problem　is　formulated　as　a　nonlinear　programming　problem　by　jointly　optimizing　transmitting　power,　system　bandwidth,　and　computing　resources.　The　objective　is　to　minimize　system　energy　consumption　while　satisfying　the　constraints　of　energy　consumption,　computing　resource,　and　transmitting　power.　Genetic　algorithm　(GA)　and　nonlinear　programming　methods　are　combined　to　obtain　the　optimal　solution　to　the　formulated　optimization　problem.　Simulation　results　demonstrate　that　the　system　energy　consumption　can　be　reduced　to　some　extent　under　our　proposed　approach　compared　with　the　traditional　GA　and　the　partial　fixed　power,　system　bandwidth,　or　computing　resources　method　based　on　GA　and　nonlinear　programming.　©　2021　IEEE.