This　paper　considered　the　constrained　unmanned　aerial　vehicle　(UAV)　path　planning　problem　as　the　multi-objective　optimization　problem,　in　which　both　costs　and　constraints　are　treated　as　the　objective　functions.　A　novel　multi-objective　particle　swarm　optimization　algorithm　based　on　the　Gaussian　distribution　and　the　Q-Learning　technique　(GMOPSO-QL)　is　proposed　and　applied　to　determine　the　feasible　and　optimal　path　for　UAV.　In　GMOPSO-QL,　the　Gaussian　distribution　based　updating　operator　is　adopted　to　generate　new　particles,　and　the　exploration　and　exploitation　modes　are　introduced　to　enhance　population　diversity　and　convergence　speed,　respectively.　Moreover,　the　Q-Learning　based　mode　selection　logic　is　introduced　to　balance　the　global　search　with　the　local　search　in　the　evolution　process.　Simulation　results　indicate　that　our　proposed　GMOPSO-QL　can　deal　with　the　constrained　UAV　path　planning　problem　and　is　superior　to　existing　optimization　algorithms　in　terms　of　efficiency　and　robustness.