The manufacturing revenues significantly depend on both the price of the order and the manufacturing schedule. Academic results on proper and industrially feasible simultaneous dynamic pricing and scheduling are scarce. This partially happens due to the different natures of the problems in the industrial setting, where a dynamic price is modeled continuously. In contrast, an industrially viable scheduling problem is inherently discrete and combinatorial in nature. In this work, we bridge the gap between the problems of dynamic pricing and scheduling by applying genetic heuristics, sampling, and joint optimization.