The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Theoretical analysis indicates that the proposed algorithm converges to the optimal solution as long as sufficient planning time is given. Planning results on robotic systems with $\mathbb{SE}(3)$ and $\mathbb{R}^7$ state spaces in challenging environments highlight the superior performance of the proposed algorithm over various state-of-the-art sampling-based planners in both static and dynamic motion planning tasks. The experiment of planning for a Turtlebot 4 operating in a dynamic environment with several moving pedestrians further verifies the feasibility and advantages of the proposed algorithm.