The DE-MCZ algorithm is an improvement of the DE-MC method, which joins the differential evolution with the theory of the Markov chains. It aims to ensure the numerical effectiveness and the convergence speed of the special variant of the Metropolis-Hastings algorithm with the help of an additional, self-adapting initial matrix. In this paper, we add the modes detection procedures to the DE-MCZ algorithm to increase its abilities in sampling from multimodal target densities. As our numerical experiments suggest, the obtained DE-MCmodes algorithm provides results that give a better fit to the desired target density than the classical approaches.