Salp　swarm　algorithm　is　a　new　meta-heuristic　algorithm　which　has　excellent　advantages　for　solving　the　multidimensional　optimization　problem.　In　this　paper,　a　hybrid　model　assisted　evolutionary　algorithm　for　solving　engineering　design　optimization　problems　is　proposed　and　investigated.　The　purpose　of　the　optimizer　is　to　improve　the　potential　shortcomings　of　the　basic　salp　swarm　optimization,　including　trapping　in　local　or　deceptive　optima　easily.　On　the　one　hand,　quantum　behavior　can　increase　an　individual＇s　searchability,　which　can　promote　the　overall　optimization　trend.　On　the　other　hand,　the　elite　opposition-based　learning　strategy　is　used　to　enhance　the　diversity　of　the　population.　Besides,　mutation　mechanism　is　introduced　to　prevent　the　individuals　of　the　population　from　being　in　stagnation　behavior.　The　proposed　quantum-behaved　and　wavelet　mutation　salp　swarm　algorithm　(QSSA)　is　applied　on　twenty-three　benchmark　functions　and　three　basic　constrained　engineering　problems.　Experimental　results　demonstrate　that　the　algorithm　has　excellent　solution　quality,　and　it　can　overcome　the　defect　of　the　low　convergence　rate.