A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these networks of embedded sensors and processors represent a major challenge for the application development. In this paper we focus on ﬁnding optimal VSN conﬁgurations which are basically given by (i) the selection of cameras to sufﬁciently monitor the area of interest, (ii) the setting of the cameras’ frame rate and resolution to fulﬁll the quality of service (QoS) requirements, and (iii) the assignment of processing tasks to cameras to achieve all required monitoring activities. We formally specify this conﬁguration problem and describe an efﬁcient approximation method based on an evolutionary algorithm. We analyze our approximation method on three different scenarios and compare the predicted results with measurements on real implementations on a VSN platform. We ﬁnally combine our approximation method with an expectation-maximization algorithm for optimizing the coverage and resource allocation in VSN with pan-tilt-zoom (PTZ) camera nodes.