Electrical impedance tomography (EIT) is an imaging method for characterizing the inner conductivity distribution of an object based on the measured boundary voltages resulting from the injection of an AC signal, followed by an image reconstruction procedure. An algorithm tries to solve an ill-posed inverse problem making it challenging to reconstruct an accurate image. To overcome this, we propose a genetic algorithm (GA) for the image reconstruction with a non-blind search method considering prior knowledge about the possible conductivity distribution in the initial search space. To validate the algorithm, experiments have been conducted in a water tank. The algorithm’s performance was evaluated regarding image quality and processing time, being able to minimize the corresponding quality function to 0.0505 with 100 generations using the non-blind search and the uniform crossover/random mutation. Compared to traditional methods, the GA achieves significantly better image quality. It has been implemented as an image reconstruction algorithm for gesture recognition. EIT measurements have been conducted with six persons performing American sign numbers (0–9) resulting in 1800 reconstructed images. They were classified by a previously developed convolutional neural network (CNN), reaching a 92 % accuracy, which is a very good achievement in the case of multiple subjects.