Low-Density Parity-Check (LDPC) codes are usually decoded by running a Belief-Propagation (BP), or message-passing, algorithm over the factor graph of the code.  The cyclic nature of LDPC factor graphs makes the algorithm both non-optimal and iterative, thus requiring a message-passing schedule. A well-known problem in loopy BP LDPC decoding is the presence channel errors in certain graph structures, called trapping sets, that do not allow the decoder to converge. We propose to overcome these problems by means of a better scheduling of the loopy BP messages. The traditional message-passing schedule consists of updating all the variable nodes in the graph, using the same pre-update information, followed by updating all the check nodes of the graph, again, using the same pre-update information. Recently several studies show that sequential scheduling, in which messages are generated using the latest available information, significantly improves the convergence speed in terms of number of iterations. Sequential scheduling raises the problem of finding the best sequence of message updates. This paper presents practical scheduling strategies that use the value of the messages in the graph to find the next message to be updated. Simulation results show that these informed update sequences require significantly fewer iterations than standard sequential schedules. Furthermore, we show that informed scheduling solves some standard trapping set errors and therefore, it also outperforms traditional scheduling for a large numbers of iterations.