The　prediction　model　for　mechanical　properties　of　RAC　was　established　through　the　Bayesian　optimization-based　Gaussian　process　regression　(BO-GPR)　method,　where　the　input　variables　in　BO-GPR　model　depend　on　the　mix　ratio　of　concrete.　Then　the　compressive　strength　prediction　model,　the　material　cost,　and　environmental　factors　were　simultaneously　considered　as　objectives,　while　a　multi-objective　gray　wolf　optimization　algorithm　was　developed　for　finding　the　optimal　mix　ratio.　A　total　of　730　RAC　datasets　were　used　for　training　and　testing　the　predication　model,　while　the　optimal　design　method　for　mix　ratio　was　verified　through　RAC　experiments.　The　experimental　results　show　that　the　predicted,　testing,　and　expected　compressive　strengths　are　nearly　consistent,　illustrating　the　effectiveness　of　the　proposed　method.