Genetic　algorithm　is　one　of　primary　algorithms　extensively　used　to　address　the　multi-objective　flexible　job-shop　scheduling　problem.　However,　genetic　algorithm　converges　at　a　relatively　slow　speed.　By　hybridizing　genetic　algorithm　with　particle　swarm　optimization,　this　article　proposes　a　teaching-and-learning-based　hybrid　genetic-particle　swarm　optimization　algorithm　to　address　multi-objective　flexible　job-shop　scheduling　problem.　The　proposed　algorithm　comprises　three　modules:　genetic　algorithm,　bi-memory　learning,　and　particle　swarm　optimization.　A　learning　mechanism　is　incorporated　into　genetic　algorithm,　and　therefore,　during　the　process　of　evolution,　the　offspring　in　genetic　algorithm　can　learn　the　characteristics　of　elite　chromosomes　from　the　bi-memory　learning.　For　solving　multi-objective　flexible　job-shop　scheduling　problem,　this　study　proposes　a　discrete　particle　swarm　optimization　algorithm.　The　population　is　partitioned　into　two　subpopulations　for　genetic　algorithm　module　and　particle　swarm　optimization　module.　These　two　algorithms　simultaneously　search　for　solutions　in　their　own　subpopulations　and　exchange　the　information　between　these　two　subpopulations,　such　that　both　algorithms　can　complement　each　other　with　advantages.　The　proposed　algorithm　is　evaluated　on　some　instances,　and　experimental　results　demonstrate　that　the　proposed　algorithm　is　an　effective　method　for　multi-objective　flexible　job-shop　scheduling　problem.