This paper presents a parallel evolutionary algorithm to solve reliability problems with accuracy and repeatability of results. The last characteristic is usually overlooked; however, it is critical to the reliability of the calculation method itself. Note that evolutionary algorithms are stochastic processes and may not always generate identical results. The optimisation problem resulting from the first order reliability method is considered with an implicit state function that can include a call to a finite element analysis (FEA). A strategy to handle failures from the transformation of random variables or from the finite element call during the evolution process is explained in detail. Several benchmark tests are studied, including some involving bounded random variables that introduce strong non-linearities in the mapping to standard Gaussian space. In addition, the solutions of 2D and 3D frame problems using the finite element method illustrate the capabilities of the algorithm including the convenience of the algorithm in handling discrete limit state functions. Finally, the ability to obtain similar results after many runs is demonstrated.