Abstract: A node scheduling algorithm based on enhanced version of coral reef optimization algorithm (shortly for ECRO) is proposed to solve the life maximization problem of heterogeneous directional sensor networks for connectivity and differentiated target coverage requirements. Based on cover sets theory, ECRO is utilized to get the cover sets, which can cover all the targets and satisfy their connectivity and coverage quality requirements. The improvement of coral reef optimization (shortly for CRO) lies in the three aspects. Firstly, the population is initialized by the SOBOL sequence and an opposition learning strategy. Secondly the operator of harmony search algorithm, immigration in biogeography-based optimization and a self-adaptive mutation strategy in differential evolution algorithm are introduced into the brooding procedure of the coral larvae formation to conserve the excellent solutions of the population and enhance the diversity of the descent and the ability of optimization for coral reef. Moreover, an opposition learning strategy and differential strategy with the optimal individual are utilized to improve the performance of the worst individual of the population. Extensive simulation experiments both in numerical benchmark functions and node scheduling are conducted to validate the proposed ECRO. The results show that the proposed ECRO outperforms the compared algorithms, which demonstrate the superiority of the proposed algorithm ECRO.