Nowadays although, most of the signal processing is done in digital domain, analog circuits are still basis of many complex and vital systems. One of the methods to design analog circuits is the use of evolvable hardware technique. The field of evolvable hardware has connection to the fields of computer science, biology and electronic engineering. In evolvable hardware technique, circuit is evolved automatically instead of being designed directly. This technique uses a reconfigurable device and an evolutionary algorithm. In this work, a Field Programmable Analog Array (FPAA) is used as a reconfigurable device and genetic algorithm, that is the most prevalent evolutionary algorithm used by evolvable hardware practitioners, is used as an evolutionary algorithm. Field Programmable Analog Arrays are analog programmable devices with the same level of in-system configuration flexibility that their digital counterparts FPGAs have in the digital systems. Like FPGAs, FPAAs have the advantages of, quick reconfiguration due to their software support, allowing for easy design, debugging and implementation of various functions. Genetic algorithms play an essential role in optimization and search problems especially when the complexity and diversity of the search space is too high. Each individual in the GA represents a solution to the problem. And each individual contains a number of genes depending on the problem; and the parameters of the problem will be encoded to the genes. At the beginning a random population of individuals is generated. Each individual is evaluated based on a fitness function, and based on fitness values of individuals and depends on the selection operator a group of them (surviving population) will be selected. Based on the selected group if the stop criterion is satisfied the algorithm is over, otherwise the genetic operators are applied to the surviving population. In this case a crossover operator combines features of two individuals to produce new offsprings, and mutation is applied to introduce new variety of offsprings and to increase diversity. This process iterates until the algorithm converges to the desired solution or until another stop criterion that was set by the user is reached. In this work, evolvable hardware technique is used to optimize an analog circuit and also to design parts of an analog circuit. For the latter one, this approach is used to find the near optimum parameters of a PID controller for a second order plant. The PID controller is implemented on a FPAA and the Keywords: Evolvable Hardware, FPAA, Genetic Algorithm, PID controller, AM modulation circuit