Spatially resolving the cellular basis of complex human traits is essential for elucidating disease mechanisms, yet the comparative performance of computational methods for this task has not been systematically evaluated. Here, we present SMECT (Spatial Mapping Evaluation of Complex Traits), the first comprehensive framework for systematically evaluating methods that integrate genetic data with spatial transcriptomics. SMECT combines a biologically realistic simulation engine, a curated resource of 21 diverse real-world datasets, and a multi-faceted assessment toolkit. Using this framework, we benchmarked three state-of-the-art methods. DESE, S-LDSC, and scDRS across 19 complex traits. Our analysis reveals a fundamental trade-off between detection sensitivity and biological specificity. We demonstrate that while S-LDSC identifies extensive spatial signals, it suffers from inflated non-specific significant associations. Conversely, scDRS is highly specific but conservative, performing well only in tissues with strong biological signals while missing subtle associations in sparser datasets. DESE overcomes these limitations, consistently achieving high power and robust specificity across both simulated and real-world scenarios. SMECT provides critical guidelines for method selection and serves as a foundational resource for developing robust spatial analyses of human complex traits. The framework is publicly available at https://github.com/pmglab/smect.