Mobile adhoc network (MANETs) comprises a collection of independent, compact sized, and inexpensive sensor nodes, which are commonly used to sense the physical parameters in the geographical location and transmit it to the base station (BS). Since clustering and routing are considered as the commonly used energy efficient techniques, several metaheuristic algorithms have been employed to determine optimal cluster heads (CHs) and routes to destination. But most of the metaheuristic techniques have failed to achieve effective clustering and routing solutions in large search space and the chance of generating optimal solutions is also considerably reduced. To resolve these issues, this paper presents a new Metaheuristic Quantum Glowworm Swarm Optimization based Clustering with Secure Routing Protocol for MANET, named QGSOC-SRP. The presented QGSOC-SRP technique follows two stage processes, namely optimal CH selection and route selection. Firstly, the QGSO algorithm derives a fitness function using four variables such as energy, distance, node degree, and trust factor for optimal election of secure CHs. Secondly, the SRP using oppositional gravitational search algorithm (OGSA) is applied for the optimal selection of routes to BS. The traditional GSA is inspired by the law of gravity and interaction among masses. To improve the effectiveness of the GSA, OGSA is derived based on the oppositional based learning concept for population initialization and generation jumping. For validating the effective results of the presented OGSOC-SRP technique, a set of experiments were performed and the results are determined interms of distinct measures.