Internet of Things (IoT) assisted wireless sensor network (WSN) finds its applicability in several real-time tracking and surveillance applications. However, it suffers from various issues such as restricted battery capacity, repeated interruptions owing to multi-hop data transmission, and limited communication range. Gathering and multihop directing are considered effective solutions to complete enhanced energy competence and a generation of IoT-assisted WSN. An NP-hard problematic that can be handled with an evolutionary algorithm is the collection of the cluster head (CH) and the best potential paths to the goal. Both of these problems involve finding the optimum route to the target (EA). In this context, this study presents the design of the Energy Efficient Evolutionary Algorithm-based Clustering with Route Selection (EEEA-CRS) Protocol for Internet of Things-Assisted Wireless Sensor Networks (IoT-Assisted WSN). The EEEA-CRS technique that has been proposed has the primary intention of enhancing the energy efficiency as well as the lifetime of the IoT-assisted WSN. The EEEA-CRS approach that has been presented is broken down into its basic parts, which are the Fuzzy Chicken Swarm Optimization based Clustering (FCSO-C) phase and the Biogeography Optimization-based Multihop Routing phase (BBO-MHR). The FCSO-C technique that has been suggested chooses CHs with the use of a fitness function that takes into account residual energy, inter-cluster distance, and intra-cluster detachment. In adding, the BBO-MHR strategy identifies the optimum pathways to BS by taking into account the costs of communicating with other clusters, both within and between them. A number of different simulations were carried out in order to demonstrate that the EEEA-CRS methodology yields superior results. The EEEA-CRS method was shown to be superior to other methods in use today, according to the findings of an exhaustive comparison and study.