<p>While the security of virtual machines is the prime issue in the cloud environment. In this paper, an energy and security-aware task scheduling algorithm is proposed. It is based on the atom search optimizer (ASO) and the quantum evolutionary algorithm (QEA). This paper uses a load monitoring component to determine under-loaded and overloaded virtual machines. The proposed algorithm (FQASO) combines the ASO with the concepts of quantum computing (i.e., a quantum bit and superposition of states). To increase the search sufficiency of quantum computing, we use a fuzzy coordinate rotation gate in updating individuals. The exploration capabilities of QEA can be used to improve the search capabilities of ASO. An evaluation of the proposed method is divided into two parts. The first part evaluates the FQASO as a meta-heuristic algorithm on eight benchmark functions (including unimodal and multimodal problems) and compares the results with four current algorithms (including ASO, FMPSO, MFGSO, and SCEHO-IPSO). The best fitness metric and the average population fitness metric are used in this section. In unimodal and multimodal problems, the proposed method reduces the objective function by 5.7 and 8.2% on average, respectively. The second part compares FQASO with state-of-the-art methods such as FMPSO, MOWS, ASO, MFGSO, SCEHO-IPSO, SAEA, and GAGWO. FQASO consistently outperforms these methods, saving more than 30.57% of energy while reducing security risks by 53.47%. Compared to SAEA and GAGWO, FQASO shows a 10–25% improvement in makespan and energy efficiency, maintaining lower energy consumption and execution times while achieving competitive security performance. Based on this comprehensive evaluation, FQASO performs significantly better than existing algorithms like SAEA and GAGWO in terms of energy efficiency, makespan, and security management.</p>