In object detection, the goals of successfully discriminating between different kinds of objects (object classification) and accurately identifying the positions of all objects of interest in a large image (object localisation) are potentially in conflict. We propose a Multi-Objective Genetic Programming (MOGP) approach to the task of providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate. Experiments on two datasets, simple shapes and photographs of coins, show that it is difficult for a Single-Objective GP (SOGP) system (which weights the multiple objectives a priori) to evolve effective object detectors, but that an MOGP system is able to evolve a range of effective object detectors more efficiently.