The coverage optimization of wireless visual sensor networks (WVSNs) with changeable field of views (FOVs) brings more challenges on the decision making of modern cyber–physical systems as it is a multi-objective decision problem where some contradictory indices, e.g. the sensor coverage and the redundancy, should be taken into consideration concurrently. In this paper, a novel multi-species evolutionary algorithm (MSEA) is proposed to address this issue by introducing a multi-species evolution scheme to enhance the search ability. A competition mechanism based on the deductive sort and the crowding distances is developed to facilitate the generation of Pareto front and the elitist individuals are evolved from a multi-species hybrid population. Comparative results show a better balancing performance between the exploration and the exploitation of the proposed algorithm which induces a strong approximation to the feasible WVSN managements.