Degradation measurements of highly reliable products can be used to extrapolate the failure times, and subsequently, predict the remaining useful time‐to‐failure distribution. Different degradation failure mechanisms should be treated specifically to achieve an accurate estimation and prediction. In this study, a typical sequential hard and soft failure mode is investigated while applying the lifetime delayed degradation processes (LDDP) modeling framework. This study focuses on the gamma process, which is combined with location‐scale‐family lifetime distributions and a general reliability inference approach that is applied based on the likelihood. Furthermore, the optimal inspection planning issue with the corresponding process is formulated. The determination of Fisher information (D‐optimality) is used to evaluate the efficiency of the different inspection plans to obtain more information during the degradation process. This study consisted of comprehensive simulations with a more accurate mean time‐to‐failure (MTTF) estimation for the reliability analysis and the inspection starting time that is optimized for planning. A practical crack inspection dataset is also used in this investigation. Herein, the optimal time was simplified while considering the first inspection time and the equivalent inspection interval. It has been proved from the simulation that the inspection number affects the peak value of the Fisher information. This study helps to reduce the experiment sample size and improve the estimation of reliability effectively in engineering.