This work proposes an approach to sensor network design and retrofitting that combines quantitative process knowledge and fault tree analysis into a new methodology for evaluating the reliability of process variable estimation, taking into account both hardware and functional redundancy. The reliability of estimating process variables is used to determine the sensor network reliability, which, in turn, is used for the design and retrofit of the network. Thus, a reliable sensor network design and retrofit problem is formulated as a mathematical programming optimization problem and solved using genetic algorithms. The performance of this proposed methodology is compared with the current approaches using different case studies and handling several scenarios.