Most swarm intelligence algorithms are stochastic metaheuristic algorithms in nature, and thus they may not solve all optimisation problems perfectly. Different algorithms may have different advantages, and the different real cases should be analysed independently. In this paper, a new brick-up re- building method for metaheuristic algorithms is proposed and discussed. This brick-up method creatively separates the metaheuristic algorithms into components (bricks) and generate a brick pool for further use. Then a new and best fitting algorithm will be generated custom-made to different problem and suggested to user as the best solution available in metaheuristic design. The main contributions for this research are the metaheuristic brick selection rules analysis and brick-up system model simulation. The proposed model has been tested on CEC 2015 benchmark function sets to verify its performance. The experimental results show that this recombination model can produce a metaheuristic algorithm that is as efficient as each individual candidate algorithm or better.