This thesis describes work using evolutionary robotics methods to evolve 'minimal' developmental systems. Evolutionary robotics ha.c; a proven track record as a methodology for automating the design of systems capable ofnon-tri.vi.aJ adaptive behaviour. In addition evolutionary robotic~ has been used by a number of researchers as a means to investigate the evolution of adaptive behaviour .in natural systems. In this th.esis evolutionary robotics methods are adopted for the exploration of the development of adaptive behaviour. This approach isfouDded on. the notion that developing agents and their contexts comprise systems char.acterised by complex dynamic interactions instan.tiated at mUltiple tim.e-scales.. A discussion is presented of the evidence from psychology and' biology which has informed the perspecti.ve advanced here; specifically that developing agent') are best understood a.c; systems and that the context in which much of the development observed in higher ani.mals occurs is fundamentally social. . The thesis also includes discussion of the adaptive systems and evolutionary robotics research which has contributed to the methodology proposed in this work. Three pieces of empirical work using this methodology are presented. In the first two of these experiments the use of evolutionary robotic~ as a method for obtaining 'minimal' developmental systems is assessed. Results of these investigations indicate that manipulation of context can be used to obtain developmental systems which extend beyond the boundary of the developing individual. The third set of experiments described here takes a slightly different approach, asking if this methodology can be used to evolve a specific devel.opmental trajectory, i.e. the A not B error observed in delayed manual search tasks with human infants. This model demonstrates successful performance of the delayed manual search task and includes investigation of the role of homeostatic dynamics in perseveralivc task errors. In this work the value of the proposed approach for modelling complex empirical data is as. sessed. The thesis concludes with proposals for further development of this approach and discussion of th.e potential for 'genentti.ve' modelling in which minimal developmental sy~tem models are used to suggest new experimental manipulations in. natural adaptive systems.