Previous studies concluded that the best performance from an evolutionary programming (EP) algorithm was obtained by tuning the parameters for each problem. These studies used fitness at a pre-specified number of evaluations as the criterion for measuring performance. This study uses a complete factorial design for a large set of parameters on a wider array of functions and uses the mean trials to find the global optimum when practical. Our results suggest that the most critical EP control parameter is the perturbation method/rate of the strategy variables that control algorithm search potential. We found that the decline of search capacity limits the difficulty of functions that can be successfully solved with EP. Therefore, we propose a soft restart mechanism that significantly improves EP performance on more difficult problems.