Damage identification in ship structures is traditionally performed through on-site inspections. In this work, a first step is made towards assessing an in-line with operation ship hull Structural Health Monitoring system by registering onboard sensor data. Specifically, an optimization-based approach is proposed for solving the inverse problem for damage identification through processing static response data. Idealized geometry and loading conditions are considered for the deck and shell plating. Damage is abstractly represented as a single circular hole randomly located within the defined domain. Strain readings representing onboard measured data are provided by a FE model developed for this purpose. These correspond to zero-strain paths for each considered case: axial strains along the ship’s neutral axis on the side shells and shear strains along the deck’s centerline. Damage detection amounts to predicting its location, essentially considered the design variable of an optimization problem seeking to minimize an error function between strains measured for various damage scenarios and an indicative target scenario. Three established optimization algorithms are used for this task: a gradient-based, a Genetic Algorithm-based and a statistics-based method (Design of Experiments and Response Surface Methodology). Results indicate that the gradient and GA based approaches are more efficient while the less efficient statistics-based approach proved less computationally demanding.