In this paper, we propose a novel autonomous intelligent tool for the optimum design of a wireless relayed communication network deployed over disaster areas. The so-called dynamic relay deployment problem consists of finding the optimum number of deployed relays and their location aimed at simultaneously maximizing the overall number of mobile nodes covered and minimizing the cost of the deployment. In this paper, we extend the problem by considering diverse relay models characterized by different coverage radii and associated costs. To efficiently tackle this problem we derive a novel hybrid scheme comprising: (1) a Harmony Search (HS)-based global search procedure and (2) a modified version of the well-known K-means clustering algorithm as a local search technique. Single- and bi-objective formulations of the algorithm are proposed for emergency and strategic operational planning, respectively. Monte Carlo simulations are run over a emulated scenario based on real statistical data from the Castilla La Mancha region (center of Spain) to show that, in comparison with a standard implementation of the K-means algorithm followed by a exhaustive search procedure over all relay-model combinations, the proposed scheme renders on average better coverage levels and reduced costs providing, at the same time, an intelligent tool capable of simultaneously determining the number and models of the relays to be deployed.