How to tackle technology adoption and capacity planning simultaneously under uncertainty is a challenging issue for industries to improve their competitiveness. In this research, a technology portfolio adoption model considering capacity planning under demand and technological uncertainties is proposed. The model optimizes technology portfolio and simultaneously addresses the capacity planning to maximize the profit of a firm over a planning horizon. The problem is modeled by Markov decision process (MDP), of which each action is presented as a desired length of time to retain the currently used technologies and the corresponding capacity plan. Each action is modeled by a stochastic mixed integer programming (SMIP) problem. For achieving an efficient solution, a sampling-based hybrid algorithm called PSO–DE, which integrates the particle swarm optimization (PSO) algorithm with the differential evolution (DE) algorithm, is employed to solve the SMIP problem. After that, an optimality backward recursive function is employed to solve the MDP problem. Further, a parallel computing technique is utilized to relax the computational burden of the MDP model. A sensitivity analysis is conducted to investigate effects of the algorithm parameters by using Taguchi method. A performance comparison among DE-PSO and other popular algorithms is conducted. Finally, we evaluate the impact of different levels of demand variance and risk of investment on the expected profit.