The Bees AlgorithmBees algorithm (BA) hasGenetic Bees Algorithm (GBA) proven its effectiveness in performing optimisationOptimisation in many problems in recent years with many applications, but the global search of the BA is not its strength. This chapter summarised reports on a hybridHybrid model called the Genetic Bees Algorithm (GBA)Genetic Bees Algorithm (GBA) to enhance the BA for both continuous and combinatorial optimisationCombinatorial optimisation problems. For continuous problemsContinuous problem, GBA in training a multilayer perceptron (MLP)Multilayer Perceptron (MLP) is reported for the first time to solve real-world problems. Experimental results show that GBA provides significantly better performance than Particle Swarm OptimisationOptimisation (PSO) in training MLP with higher accuracy. For combinatorialCombinatorial problems, in the two-stage flow shop scheduling problemFlow shop scheduling problem (TFSSP), three methods, such as Particle Swam Optimiser (PSO), Genetic AlgorithmAlgorithms (GA) and GBA, are compared. GBA is competitive and better than PSO and GA in a majority of instances in terms of results, proving that GBA is a realistic and efficient solution to the TFSSP. Finally, GBA proves its strength in solving theJust-in-Time Job Shop Scheduling Problem (JITJSSP) Just-in-Time Job Shop Scheduling Problem (JITJSSP) by solving a set of 36 benchmark instances ranging from 20 to 200 operations, the outcomes obtained from that are then compared to an exact method, two recent studies, and best-known solutions. The results show that the GBA has its strengths and weaknesses in solving the JITJSSP, and it performs well in some instances but does not perform well in others.