A parallel solution to the implementation of evolutionary algorithms is proposed, where the most costly part of the whole evolutionary algorithm computations (the population evaluation), is deported to a GPGPU card. Experiments are presented for two benchmark examples on two models of GPGPU cards: first a "toy" problem is used to illustrate some noticable behaviour characteristics before a real problem is tested out. Results show a speed-up of up to 100 times compared to an execution on a standard micro-processor. To our knowledge, this solution is the first showing such an efficiency with GPGPU cards. Finally, the EASEA language and its compiler are also extended to allow users to easily specify and generate efficient parallel implementations of evolutionay algorithms using GPGPU cards.