In this article, the optimal plans for a k-level constant-stress accelerated life test are presented for extension of the exponential failure data under complete sampling. According to the log-linear life-stress relationship, the optimal proportion of test units allocated to each stress level is determined under D- and C-optimality criteria. Moreover, a real data set is analyzed to illustrate the proposed procedures. Furthermore, the real data set is used to show that extension of the exponential (EE) distribution can be a better model than both Weibull distribution and generalized exponential distribution. In addition, numerical examples are used to illustrate the proposed procedures and to compare between the D-optimal plan and C-optimal plan through asymptotic variance of maximum likelihood estimators. Finally, some interesting conclusions are obtained.