Mobile robots can be used to deploy static wireless sensor nodes to achieve the coverage and connectivity require-ments of the applications considered. Many solutions have been provided in the literature to compute the set of locations where the sensor nodes should be placed. In this paper, we show how this set of locations can be used by a mobile robot to optimize its tour to deploy the sensor nodes to their right locations. In order to reduce both the energy consumed by the robot, its exposure time to a hostile environment, as well as the time at which the wireless network becomes operational, the optimal tour of the robot is this minimizing the delay. This delay must take into account not only the time needed by the robot to travel the tour distance but also the time spent in the rotations performed by the robot each time it changes its direction. This problem is called the Robot Deploying Sensor nodes problem, in short RDS. We first show how this problem differs from the well-known traveling salesman problem. We then propose an integer linear program formulation of the RDS problem. We propose various algorithms relevant to iterative improvement by exchanging tour edges, genetic approach and hybridization. The solutions provided by these algorithms are compared and their closeness to the optimal is evaluated in various configurations. I. CONTEXT AND MOTIVATIONS More and more applications are supported by wireless sen-sor networks. They cover areas as diverse as structural health monitoring, smart metering, industrial process monitoring, precision farming, smart cities, control of traffic lights, smart home, etc. The main reason for this tremendous development lies in the ease of deployment of wireless sensor networks. However, to meet the application requirements in terms of coverage and connectivity while minimizing the number of wireless sensors deployed, some rules must be followed. In short, full coverage of an area means that any event occurring in this area will be detected by at least one sensor node. Connectivity means that the information related to any event detected by a sensor node can be delivered to a special wireless node, called the sink, in charge of processing the data gathered from the sensor nodes. Many papers in the literature deal with these two big issues that are coverage and connectivity, leading to various problems mainly depending on the item to cover (area, points of interest, barrier) and on the type of coverage requested (full/partial, permanent/intermittent). The interested reader can refer to [1] for a survey of these problems and their solutions. Concerning the deployment achieved to meet these appli-cation requirements, they differ in their goal, their constraints and their implementation (e.g., centralized versus distributed). Most deployments aim at minimizing the number of sensor nodes deployed for cost reasons to achieve the application requirements. Another goal that is frequently encountered in crisis situation (e.g., after a disaster) where a wireless sensor network must be fastly deployed in order to on the one hand help rescuers to save victimes and on the other hand assist in damage assessment. In such cases, the goal is to minimize the time needed to deploy an operational wireless network. This goal is also targeted in hostile environment, where the exposure duration must be reduced. For cost reasons, static sensor nodes are more frequent than mobile ones. That is why in this paper, we focus on the computation of the minimum-delay tour of a mobile robot that has to deploy static wireless sensor nodes at positions that have been previously computed to meet the application requirements in terms of coverage and connectivity.