Technological progress and uncertainty of demand are important factors of problems of capacity planning in manufacturing. The majority of previous studies did only focus on technological breakthroughs rather than technical upgrades, two different ways of the technological progress, and paid more attention on capacity expansion while less attention on replacement. This paper presents for the first time an integrated capacity planning model that takes into account both technological breakthroughs and upgrades for equipment procurement and replacement. Under stochastic demand and technological progress, we use scenario-tree method describe uncertainty of two factors above, and propose an integrated model of multi-period capacity planning problem with the objective of minimum expected total cost of capacity expansion, replacement and maintenance. Faced with the nonlinear mixed integer programming model established by the problem above, the paper designs a heuristic solution method based on genetic algorithm (GA) framework for this NP complete problem, which converts the problem into an equivalent problem by performing certain translations. The equivalent problem can be solved by GA and classic optimization. In the process of population initialization, only expansion decision variables are chosen as the evolutionary population in GA, and then we use the classical optimization method to accurately solve the optimal capacity replacement problem for each individual, and use the total cost of expansion and replacement as the individual fitness for individual evaluation and population evolution. A numerical experiment illustrates that the technological upgrades can effectively reduce the cost of capacity planning under different demand scenarios, and the heuristic algorithm proposed in this paper has good convergence stability and performance stability for solving such planning problems. [ABSTRACT FROM AUTHOR]