This research proposes and validates a Kalman filter–based method to optimize the placement of accelerometers in buildings, formulated as a multi-objective problem that simultaneously minimizes the number of sensors and the state estimation error. State-space dynamic models of 3-, 9-, 15-, and 30-story buildings were developed using an equivalent beam model, incorporating dynamic properties representative of Chilean buildings. The trace of the state error covariance matrix was employed as the performance metric, showing strong correlation with sensor signal-to-noise ratio (SNR) and the normalized absolute estimation error. The results highlight that measurement noise critically affects sensor placement. As the noise covariance increases, estimation uncertainty grows, and more sensors are required, often concentrated in specific structural regions. Conversely, high-sensitivity low-noise sensors reduce uncertainty, though at higher sensor installation costs. Maintaining an SNR above 10 dB proved essential to ensure reliable modal identification. Optimal layouts tended to concentrate sensors on upper floors, where accelerations and SNR are higher, avoiding redundant sensors at modal nodes or lower levels. Validation under real and synthetic excitations, including the 2010 Concepción earthquake ground motion and band-limited white noise, confirmed that the method can accurately identify the fundamental frequencies of the structures. These findings demonstrate the effectiveness of the proposed Kalman filter–based methodology for optimizing sensor layouts in structural health monitoring applications under realistic operational conditions.