The　path　planning　of　unmanned　aerial　vehicles　(UAVs)　in　the　threat　and　countermeasure　region　is　a　constrained　nonlinear　optimization　problem　with　many　static　and　dynamic　constraints.　The　fruit　fly　optimization　algorithm　(FOA)　is　widely　used　to　handle　this　kind　of　nonlinear　optimization　problem.　In　this　paper,　the　multiple　swarm　fruit　fly　optimization　algorithm　(MSFOA)　is　proposed　to　overcome　the　drawback　of　the　original　FOA　in　terms　of　slow　global　convergence　speed　and　local　optimum,　and　then　is　applied　to　solve　the　coordinated　path　planning　problem　for　multi-UAVs.　In　the　proposed　MSFOA,　the　whole　fruit　fly　swarm　is　divided　into　several　sub-swarms　with　multi-tasks　in　order　to　expand　the　searching　space　to　improve　the　searching　ability,　while　the　offspring　competition　strategy　is　introduced　to　improve　the　utilization　degree　of　each　calculation　result　and　realize　the　exchange　of　information　among　various　fruit　fly　sub-swarms.　To　avoid　the　collision　among　multi-UAVs,　the　collision　detection　method　is　also　proposed.　Simulation　results　show　that　the　proposed　MSFOA　is　superior　to　the　original　FOA　in　terms　of　convergence　and　accuracy.　©　2020　by　the　authors.