The purpose of this research is to propose several optimization methods for the stochastic multi-perioddisassembly lot-sizing problem. The case of one type of end-of-life product and a two-level disassemblysystem is studied. The disassembly lead times are discrete random variables with a known and boundedprobability distribution. The objective is to optimize the expected value of the total cost, which is the sumof setup cost, overload cost, inventory holding cost and backlogging cost. Three approaches were developedto solve the studied problem: (i) a two-stage mixed-integer linear programming model based on all possiblescenarios for small instances, (ii) a sample average approximation approach based on Monte Carlo simulationfor medium-scale instances and, (iii) an optimization approach based on the Monte Carlo simulation anda genetic algorithm for large-scale instances. Experimental results show the effectiveness of the proposedmodels which can be used to support decision-making on replenishment and disassembly plans.Keywords:Capacitated disassembly lot-sizing, Stochastic lead times, Monte Carlo Simulation, Sampleaverage approximation, Genetic algorithm.