Environmental monitoring is crucial for climate change research, pollution control, and disaster preparedness, yet deploying extensive sensor networks faces financial and logistical constraints. This paper addresses the Multivariate Subset Sensor Selection Optimization (MSSSO) problem, optimizing sensor selection and placement across multiple environmental variables using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). We introduce five novel sensor placement strategies that balance sensor redundancy, spatial distribution, and cost-effectiveness. Key contributions include the formulation of these strategies, the introduction of the Spatial Coverage Score (SCS) metric, and comprehensive benchmarking using real-world and synthetic datasets. Results demonstrate that the Temporal Similarity with Entropy Maximization (TSEM) strategy consistently achieves superior performance, minimizing error while maximizing spatial coverage. This work establishes a foundational framework for MSSSO with practical applicability to diverse environmental monitoring scenarios.
