This work proposes an optimal fiber optic sensor placement framework for structural health monitoring (SHM) applications. The framework is applied to an aircraft's wing spar entirely made of composite materials. The damage of interest is debonding between laminates, which may cause local buckling that results in reduced structural load carrying capabilities. A high-fidelity finite element (FE) model is used as a synthetic data generator. The inputs to the model are loads and debonding damage parameters (size and location), and the outputs are uniaxial strain measurements and buckling eigenvalues. “Run time” surrogate models are created using different machine learning methods to overcome the high computational costs of each run of the physics-based model. Then, Bayesian inference is used to estimate the damage parameters given strain measured at candidate sensor locations. These estimations are used to assess damage criticality, which is linked to buckling eigenvalues, and transformed into decisions. Bayesian optimization is used to select the candidates that minimize a utility function that considers the costs associated with making a certain decision plus the costs of acquiring and installing the SHM hardware (sensors, data acquisition system, etc.). The candidate with the lowest cost is selected. The resulting optimal sensor configuration is presented, consisting of the number of sensors to be deployed and their respective locations. The importance of defining an objective function that reflects the goal of the SHM system (e.g., maximizing the probability of detection, minimizing the probability of false alarms, or a balance of both) are also discussed.