The　high　mobility　and　maneuverability　of　unmanned　aerial　vehicles　(UAVs)　enable　them　to　act　as　temporary　base　stations　(BSs)　in　extreme　environments,　expanding　the　computing　capacities　of　terminals　for　intelligent　applications,　most　of　which　are　object-oriented　ones.　Computation　offloading　in　a　UAV-based　edge-cloud　environment　is　an　excellent　way　to　improve　the　performance　of　these　object-oriented　intelligent　applications.　In　contrast,　the　computation-intensive　tasks　are　offloaded　to　the　cloud,　and　the　data-intensive　ones　are　offloaded　to　the　edge.　Though　computation　offloading　over　the　cloud,　edge,　and　terminals　has　been　broadly　studied,　existing　researches　primarily　establish　scheduling　algorithms　on　program　high-level　abstraction　without　consideration　of　challenges　from　program　structures.　We　focus　on　task　scheduling　for　offloading　object-oriented　applications　while　considering　the　＇encapsulation＇　characteristic.　We　proposed　a　time-driven　offloading　strategy　based　on　a　particle　swarm　optimization　algorithm　employing　the　genetic　algorithm　operators　with　floating　encoding　(PGFE).　This　strategy　introduces　a　genetic　algorithm＇s　randomly　two-point　crossover　and　mutation　operator　to　avoid　converging　on　local　optima　effectively.　The　simulation　results　show　that　our　strategy　can　reduce　the　average　execution　time　of　object-oriented　applications　by　11.78-48.02%,　compared　with　other　classic　algorithms　in　a　UAV-based　edge-cloud　environment.