In　recent　years,　unmanned　aerial　vehicles　(UAVs)　have　been　widely　used　in　autonomous　cruise　missions　such　as　high-altitude　aerial　photography,　power　inspection,　and　geographic　mapping　with　the　advantages　of　flexible　maneuvering　and　high　stability.　In　order　to　ensure　that　UAVs　can　choose　the　most　efficient　and　reliable　flight　route　in　complex　environments,　this　paper　designs　a　genetic　algorithm-based　path　planning　for　UAVs　in　autonomous　cruise　operations.　The　main　contents　include:　1.　Risk　assessment　of　the　route　using　the　return　value　of　the　fitness　function　as　the　safety　factor,　and　solving　the　optimal　path　between　two　points　using　an　improved　genetic　algorithm;　2.　The　selection　of　strategies　for　multiple　landing　points　under　different　conditions　is　proposed　to　form　the　optimal　path　for　U　AV　cruising　within　range.　Through　the　path　planning　verification　experiments　in　Matlab,　the　results　show　that　the　improved　genetic　algorithm　has　a　shorter　total　path　distance　compared　with　the　traditional　genetic　algorithm,　and　the　results　have　good　feasibility　and　effectiveness.　©　2021　IEEE.