We develop an active inference route-planning method for autonomous control of an intelligent agent. The aim is to reconnoiter a geographical area while balancing search and tracking behaviors to maintain a dynamic situational awareness map. To achieve this, we construct an evidence map that reflects our current understanding of the situation, integrating both positive and "negative" sensor observations of possible target objects collected over time, and diffusing the evidence across the map as time progresses. The generative model of active inference uses Dempster-Shafer theory and a Gaussian sensor model to provide input to the agent. The generative process employs a Bayesian approach to update a posterior probability distribution. We calculate the variational free energy across all locations within the sensor radius by assessing the divergence between a pignistic probability distribution of the evidence map and a posterior probability distribution of the target object, including the level of surprisal associated with receiving new observations. Using the variational free energy, we direct the agent's movement in simulation by taking incremental steps toward a position that minimizes the variational free energy. This approach addresses the challenge of balancing exploration and exploitation, allowing the agent to search large areas while efficiently tracking identified targets. Across 1000 simulation trials, the approach surpasses benchmarks in target observation by an average of 51 %-69 % across various metrics for a 100-meter sensor radius.