Abstract: The key idea behind cultural algorithm (CA) is to explicitly acquire problem-solving knowledge (beliefs) from the evolving population and in return apply that knowledge to guide the search. In this paper, we propose a CA based on multilayer belief spaces that selects the best belief space from the multilayer belief spaces so as to apply the extracted knowledge to improve the performance of evolutionary algorithm used for constrained optimization. Examples show that the algorithm produces highly competitive results at a relatively low computational cost.