Multi-objective　model　predictive　control　(MMPC)　is　an　effective　method　to　solve　the　problem　of　nonlinear　systems　with　multiple　conflicting　control　objectives.　However,　the　MMPC　method　usually　suffers　from　the　challenge　of　high　computational　cost　for　the　determining　the　optimal　objective　weights　with　the　process　of　transforming　multiple　objectives　into　a　single　objective.　For　low　computational　cost,　an　MMPC　method　with　gradient　eigenvector　algorithm　(MMPC-GEA)　for　nonlinear　systems　is　proposed　to　comprehensively　deal　with　multiple　conflicting　control　objectives.　The　proposed　MMPC-GEA　in　the　framework　of　MMPC　is　composed　of　a　fuzzy　neural　network　(FNN)　identifier　and　a　receding　optimization　algorithm.　For　the　proposed　MMPC-GEA,　FNN　with　an　adaptive　learning　algorithm　is　devised　to　capture　the　nonlinear　characteristic　of　systems.　Moreover,　a　gradient　eigenvector　algorithm　(GEA)　is　designed　to　gain　the　optimization　solution　of　the　control　objectives　for　nonlinear　systems.　Specifically,　GEA　can　reduce　the　computationally　demanding　by　avoiding　the　determination　of　the　objective　weights.　Furthermore,　the　stability　and　control　performance　analysis　of　the　MMPC-GEA　scheme　is　provided.　Finally,　the　effectiveness　of　the　proposed　MMPC-GEA　approach　is　demonstrated　using　a　numerical　simulation　and　wastewater　treatment　process.　(C)　2022　Elsevier　Inc.　All　rights　reserved.