In this letter, the development of a method for the design of soft pneumatic actuators is described. The focus is given on the interest of using a deep learning model to explore the design space with a genetic algorithm. In particular, we propose to perform the automatic synthesis of the inner structure of pneumatic actuators using Bézier curves and Gaussian Mixture Points, to have a simple representation of the actuator genotype. This makes it possible to represent a wide variety of structures and to take into account the presence of the actuator pneumatic supply. It is shown a CNN model can interestingly be used in conjunction with FEM. FEM is being used to train initially the CNN model and for the control of accuracy, while the CNN model reduces the computational cost, offering a sufficient accuracy during the synthesis thanks to transfer learning. Through two case studies, the capacity of generating geometrically complex designs such as a double-helix network for a twisting actuator is outlined. Its possible extension and further use are also discussed.