Intelligent control systems are able to work well in uncertain nonlinear systems, mainly for: changes in the operating point, presence of environmental noise and disturbances, uncertainty in sensor measurements, miscalibration, uncertain model plant, and others. For instance, fuzzy controllers have been widely studied and applied. Recently, artificial organic controllers (AOC) have been proposed as an ensemble of fuzzy logic and artificial hydrocarbon networks. However, a weakness in AOC is the lack of training methods for tuning parameters for desired output responses in control. In this regard, this paper aims to introduce an evolutionary optimization method, i.e. particle swarm optimization, for tuning artificial organic controllers. Three objectives are proposed for automatic tuning of AOC: overall error, steady-state error and settling time of output response. The proposed methodology is implemented in the well-known cart-pole system. Also, the proposed method is applied on a one-leg unstable mechanism as case study. Results validate that automatic tuning of AOC over simulation systems can achieve suitable output responses with minimal overall error, steady-state error and settling time.